a single digital market for east africa public disclosure...

SDM EAST AFRICA

Presenting a vision strategic framework implementation roadmap and impact assessment

A SINGLE DIGITAL MARKETFOR EAST AFRICA

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B A SINGLE DIGITAL MARKET FOR EAST AFRICA

A SINGLE DIGITAL MARKET FOR EAST AFRICAPresenting a vision strategic framework implementation roadmap and impact assessment

Casey Torgusson Cecilia Paradi-Guilford Isabella Hayward Ivan Gonzalez Berenguer Pena Edgardo Sepulveda World BankMichael Kende Richard Morgan Neil Gandal David Abecassis Analysys Mason

Copyright copy 2018 International Bank for Reconstruction and Development The World Bank Some rights reserved World Bank 1818 H Street NW Washington DC 20433 Telephone +1 202-473-1000 Internet wwwworldbankorg

Standard Disclaimer

This volume is a product of the staff of The World Bank The findings interpretations and conclusions expressed in this paper do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent The World Bank does not guarantee the accuracy of the data included in this work The boundaries colors denominations and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries Nothing herein shall constitute or be considered to be limitation upon or waiver of the privileges and immunities of The World Bank all of which are specifically reserved

This report was prepared by the World Bank in partnership with Analysys Mason Limited supported by a generous grant from the Swedish International Development Agency (Sida)

The report outlines the impetus for creating a single digital market (SDM) in East Africa which would drive deeper integration and spur increased dynamism of the digital economies of six East African countries Burundi Kenya Rwanda South Sudan Tanzania and Uganda

The content of this report has greatly benefited from comprehensive regional stakeholder consultation with the governments of the six countries as well as the private sector civil society organizations development partners and academia

Multi-stakeholder workshops took place in Kenya Rwanda Tanzania and Uganda between April and May 2017 The research team has also gathered inputs through a series of bilateral meetings and a qualitative online survey

These consultations along with desk research have helped shape a joint vision and strategic framework for the SDM presented in this report including an assessment of the state of existing markets

Acknowledgments This report was researched and prepared by a team from the World Bank Group and the consulting firm Analysys Mason Limited

Analysys Mason Limited North West Wing Bush House Aldwych London WC2B 4PJ Telephone +44 (0)20 7395 9000 Internetwwwanalysysmasoncom Registered in England No 5177472

The team would like to thank the following for sharing their feedback on the report which helped strengthen the presentation and analysis Paul Noumba Um Coordinating Country Director in Charge of Regional Integration Boutheina Guermazi Practice Manager for Digital Development Tim Kelly Lead ICT Policy Specialist Deo Ndikumana Senior Operations Officer for Regional Integration Vijay Pillai Advisor Africa Region Yutaka Yoshino Program Lead and Lead Economist for Equitable Growth Finance and Institutions Yoko Doi Senior Financial Specialist and Eva Clemente Miranda Private Sector Specialist

SDM EAST AFRICA

SDM EAST AFRICA

iiiA SINGLE DIGITAL MARKET FOR EAST AFRICA

Content

Foreword iv

Executive Summary 1

1 The building blocks of the Single Digital Market 6

11 Leveraging the lessons learned from global SDM initiatives 6

12 A vision for a Single Digital Market in East Africa 6

13 The SDM strategic framework 7

14 Action andcooperation - towarda lsquoDigital Roadmaprsquo 11

2 Impetus for the Single Digital Market 14

21 Economic and jobs impact 14

22 Drivers of growth and job creation 15

3 Assessment of current digital markets 18

31 A single connectivity market 18

32 A single data market 23

33 Single online market 26

34 Key enablers 32

4 Conclusions and next steps toward implementation 38

Annex A SDM Roadmap 39

Annex B SDM Scorecard 45

Annex C Existing integration efforts 53

Annex D Economic Impact Assessment 58

Figures

Figure 1 Overview of SDM vision and strategic framework 7

Figure 2 Population size of the largest global markets 16

Figure 3 Internet users and broadband penetration rates 2017 18

Figure 4 2G 3G and 4G mobile coverage based on population 2017 19

Figure 6 Mobile broadband prices as of GNI per capita 22

Figure 7 Date of entry into force for a selection of agreements and treaties regulating the international protection of IP 24

Figure 8 Share of population that is unregistered 26

Figure 9 Penetration of financial institution accounts versus mobile money accounts 27

Figure 10 Mobile payment systems deployed in the EAC 28

Figure 11 E-Government Development Index scores 30

Figure 12 Good country scores 30

Figure 13 Access to electricity 2016 32

Figure 14 Ease of doing business index 2016ndash2017 33

Figure 15 Tech hubs and incubators by country 35

Figure 16 Adult literacy rate 2015 36

iv A SINGLE DIGITAL MARKET FOR EAST AFRICA

East Africa cannot afford to think small At the current incremental pace of economic and social advancement too many of todayrsquos youth will continue to be denied the opportunity to live up to their potential The rise of digital technologies offers a chance to disrupt this trajectory unlocking new pathways for rapid economic growth innovation job creation and access to services which would have been unimaginable only a decade ago The burgeoning tech start-up clusters in Nairobi Dar Kampala and Kigali provide inspiration for what the future could hold

Tapping into this potential will require bold visionary leadership and deeper integration of the regionrsquos digital economy and innovation ecosystem By working together and seizing opportunities to lsquoleapfrogrsquo outdated infrastructure technology and business models East African countries can position the region as a premier digital investment and innovation destination In isolation East African countries will miss out on this opportunity left behind by rapid technological advancement and rivals with large domestic or integrated regional markets and more proactive digital investment and reform strategies

To become tomorrowrsquos innovators entrepreneurs and leaders East Africarsquos youth need to be empowered with the digital skills and access to technology ideas and markets that are essential to thrive in an increasingly digitized global economy Governments need to find more nimble and effective means of delivering services and interacting with citizens Businesses need to utilize digitally centered business models to connect with the millions of customers previously out of reach due to geography or low incomes

East African countries simply do not have the size and resources to meet all these needs on their own nor the luxury of time to address these challenges gradually without falling further behind The East Africa Single Digital Market (SDM) initiative is designed to help the regionrsquos citizens businesses and governments rise to the challenge and seize opportunities at hand It aims to bring together the regionrsquos digital leaders and stakeholders to rally around a common vision for the regionrsquos digital aspirations and a coordinated blueprint that can turn this vision into reality By working together East African countries and their development partners can maximize the impact of their investments and reforms to enhance their economic competitiveness and achieve digital transformation far greater than the sum of its parts

Foreword

1A SINGLE DIGITAL MARKET FOR EAST AFRICA

Executive Summary

Objectives and Definition of a Single Digital MarketThe East Africa Single Digital Market (SDM) initiative aims to support the region to become a more deeply integrated and dynamic digital investment innovation and growth hub building on the underlying strengths of the domestic digital economies and the ongoing political and economic integration processes in East Africa as well as at the continental level It stems from a recognition that countries in the region are too small to succeed in the digital economy in isolation They need the economies of scale and network effects offered by a larger regional market to help bridge the digital divide faster within their respective countries and the region in relation to the global digital economy An SDM could accelerate the growth of technology-enabled businesses lower the cost of key telecom services and catalyze new digital services for citizens and businesses alike

An integrated East African digital market would be the ninth largest in the world based on population creating the lsquodomesticrsquo market size needed to attract digital investment and provide a larger and more familiar space for local firms to grow before having to compete in global markets It will provide the region with the heft to compete against digital giants on the continent such as Nigeria and ultimately to contend with the globally dominant digital hubs such as Silicon Valley which benefit from large domestic markets in which to scale before tackling other markets

The initiative has brought together stakeholders from the public and private sectors civil society and academia to highlight the aspirations for and benefits of creating an SDM as well as to articulate

bull A shared vision for the SDM

bull A strategic framework and roadmap for achieving the SDM and

bull A market assessment and scorecard that captures the current state of domestic markets and regional readiness to move toward an SDM as well as targets and metrics for tracking progress

The SDM initiative for East Africa draws on the successes and lessons learned from similar initiatives implemented elsewhere which includes the European Digital Single Market Yet it is adapted to the unique context challenges and opportunities of the region and within each country

An SDM for East Africa is defined as one in which cross-border barriers to providing and accessing digital infrastructure content and services are eliminated It is one in which a seamless and competitive regional digital ecosystem drives a reinforcing cycle of economic growth investment innovation job creation and improved service delivery

SDM FrameworkAchieving an SDM will require simultaneously supporting domestic development and cross-border integration of the following submarket structures which form distinct yet interconnected layers of the overall SDM These include the following

bull A single connectivity market which will remove barriers to regional telecom infrastructure and services deployment to encourage investment improve performance eliminate pricing and quality differentials between coastal and landlocked countries while simultaneously expanding access to connectivity to all

bull A single data market which will enable secure exchange storage and processing of data across borders support regional deployment of data infrastructure and drive supply and demand for data-driven services and innovation across the region

bull A single online market which will allow firms governments and citizens to access and deliver both public and private services online undertake e-commerce transactions and access digital content and information seamlessly from anywhere in the region

Advancement in each distinct market layer is expected to create a virtuous cycle Each layer builds on the other which will reinforce the development expansion and integration of the SDM and further drive access to the internet innovation job creation and growth

Joint action will also be required to foster a conducive regional enabling environment to underpin the creation and impact of an SDM including targeted support for digital skills development digital innovation and entrepreneurship improvements to regional logistics and supporting infrastructure such as energy and transport and investment promotion and access to capital Finally strong leadership and institutions supported by effective coordination communication capacity building and data collection will be necessary to spearhead the SDM initiative moving forward

2 A SINGLE DIGITAL MARKET FOR EAST AFRICA

Current Market AssessmentThe comprehensive market analysis and readiness assessment carried out for this study identified key barriers that will need to be addressed to achieve an SDM as summarized below

Despite gains to date increasing the affordability availability and adoption of high-speed internet and digital technologies remain a major challenge While mobile voice services have grown rapidly internet access is still very low in most parts of East Africa Landlocked countries are still paying higher premiums to access bandwidth of lower quality compared to their coastal neighbors

The regionrsquos data infrastructure remains underdeveloped in part due to lack of a clear legal and regulatory regime to support free flow storage and processing of data across borders and a harmonized data protection and privacy regime to ensure security of personal data The availability and standardization of public data sets to drive development of data-driven services and analytics is improving but insufficient Cybersecurity collaboration is improving but is in early stages

Innovative online services and content are emerging across the region but barriers to cross-border expansion and access by users and impediments to investment are holding back

potential Most online services originate from outside the region dominated by large firms that have the resources to navigate the web of procedures licenses taxation and other barriers which require businesses to set up relatively independent operations in each country rather than seamlessly expanding across borders The region is a world leader in digital financial services but the lack of interoperability and high fees for cross-border digital transactions discourages regional e-commerce

Key digital economy enablers such as digital literacy and more advanced workforce-ready skills logistics infrastructure and services and access to energy are quickly improving but still severely constrain demand for digital services and productivity of firms Uncoordinated taxation policy and high tax burdens across all layers of the digital value chain are likewise raising costs and discouraging investment Support for innovation and entrepreneurship are helping generate promising new ideas and start-ups but these firms still struggle to access capital and scale

Digital RoadmapTo address these constraints and harness the regionrsquos potential a lsquoDigital Roadmaprsquo of priority policy reforms and investments has been identified presented in full in Annex A and with a summary of the top priority actions presented below

Digital Roadmap Summary Top Priority Actions

SINGLE CONNECTIVITY

MARKET

Action

i Undertake coordinated public-private partnership (PPP) investments toward achieving universal broadband access across the region (regional backbones and access networkslast mile)

ii Undertake coordinated reduction in taxation and fees to stimulate investment and improve affordability and access to communications services

bull Reduce or eliminate import duties for network equipment and digital devices

bull Reduce fees and facilitate easier access to rights-of-way to lay cables and mount equipment along other public infrastructure (roadways electricity transmission lines and streetlight poles pipelines rail and so on)

bull Lower cost or free spectrum allocation for service provision in rural areas

bull Reduce or eliminate value added tax (VAT) for telecommunication services

iii Extend One Network Area (ONA) coverage to more countries and services

bull Expand ONA for voice to Tanzania

bull Expand ONA for data to Burundi Tanzania and South Sudan

iv Establish a regional backbone interconnection regime based on open access principles with rights for any licensed operator in the region to purchase wholesale capacityaccess submarine cables and a glidepath for lowering interconnection rates


SINGLE DATA MARKET

Action

i Remove undue legal and regulatory restrictions (for example data nationalization) on the free flow storage and processing of data across borders

ii Harmonize data protection privacy and data exchange laws and regulations with clear guidelines for cross-border data flows personal and sensitive data and accounting for emerging services (cloud services data analytics and so on)

iii Digitize key government registries using regional standards to enable cross-border interoperability and implement a regional open data initiative using shared data standards to make regional data sets available for public and private sector data-based services and analytics

iv Establish a regional cybersecurity task force

SINGLE ONLINE MARKET

Action

i Establish mutual recognition of national digital IDs and a regional platform for identification verification by governments and digital services providers

ii Establish full interoperability between mobile money networks (domestically and regionally) with a glidepath toward lower exchange fees for cross platform and cross-border transactions

iii Adopt harmonized e-transactions and consumer protection laws based on the East Africa Community (EAC) Electronic Transactions Act

ENABLING REGIONAL

ENVIRONMENT

Action

i Establish Regional Centers of Excellence for advanced information and communication technology (ICT) education and research

ii Expand rapid technology skills training such as coding boot camps and support to mass digital literacy programs

iii Develop the lsquoDigital East Africarsquo brand and enhance regional innovation and entrepreneurship support networks

iv Develop regional investment promotion strategy and marketing campaign

v Facilitate lsquoupskillingrsquo and networking of national and regional tech hubs incubators and accelerators

vi Cultivate a regional pipeline of start-ups with demonstrated proof of concept ready to attract international venture capitalangel investors

vii Establish a regional tech entrepreneur association to facilitate government-industry dialogue


Economic Impact AssessmentThe impact on gross domestic product (GDP) growth job creation and poverty reduction is also expected to be significant implementing an SDM is estimated to generate up to a US$26 billion boost in GDP and 45 million new jobs and strongly benefit those at the bottom of the pyramid

These estimates are conservative as they do not fully take into account the spillover effects from a more integrated and competitive regional market which is expected to increase innovation technology adoption and investment across all sectors over the medium to long term fueling a reinforcing cycle of productivity gains growth and job creation for many years to come

While the countries of East Africa feature differing levels of digital maturity all stand to gain significantly from an SDM Firms and innovators will gain greater access to the regional market and a larger pool of online consumers and venture capital Citizens will gain access to more relevant and lower-cost digital content and services and the skills and opportunities to participate and thrive in the new economy Governments will be better equipped to meet their economic development and service delivery

aspirations Notably the economic analysis suggests that those countries currently lagging in connectivity will enjoy the most significant boost from an integrated digital market

Next StepsThis report serves as a first step in the SDM initiative helping concerned parties to rally around a joint vision and overarching framework The intention is now to identify programs and resources to support countries in the region to implement the priority actions identified as part of the Digital Roadmap

By taking proactive action and working together rather than in competition the SDM can be a win-win for all countries in East Africa Actions proposed will capitalize on the momentum of ongoing regional integration initiatives such as the successful ONA roaming agreement while unlocking opportunities for accelerating the wider East African integration agenda The initiative will leverage and work through existing regional institutions and platforms such as the East Africa Communications Organization (EACO) EAC Northern Corridor Committee and at the continental level through the African Continental Free Trade Area and Smart Africa Alliance

Figure ES1 Key components of the SDM report

SDM VISION CURRENT STATE ASSESSMENT

SDM SCORECARD

POLICY TOOLS

DIGITAL ROADMAP

INVESTMENT

TARGET INDICATORS

ENABLING ENVIROMENT

CURRENT STATE ASSESSMENT ACTIONS TO IMPACT INDICATORS

BURUNDI

KENYA

TANZANIA

UGANDA

SOUTH SUDAN

RWANDA

SINGLE ONLINEMARKET

SINGLE DATAMARKET

SINGLECONNECTIVITYMARKET


Introduction

Rising mobile phone penetration improving broadband Internet connectivity and widespread adoption of mobile money across East Africa are changing the way the region communicates collaborates and transacts This digital evolution has spurred development of a small but rapidly growing tech sector particularly in urban hubs with innovative entrepreneurs launching new digitally enabled services and creating 21st century jobs More significantly adoption of digital technology is gradually driving productivity gains in traditional brick and mortar industries Governments across the region have likewise begun to take advantage of these trends by moving public services online utilizing data to improve policy making and digital platforms to increase the efficiency of public service administration

While this digital evolution shows significant promise it is not yet amounting to a revolution Access to the internet is still very low by international standards Internet user figures reported by the International Telecommunication Union (ITU) suggest that only 17 percent of people in the region were online in 20171 Though it is improving internet service quality remains poor in most countries and unaffordable to large parts of the population For East Africa to benefit from the transformational social and economic benefits generated by digital technology and increasing connectivity or so-called lsquodigital dividendsrsquo much more needs to be done to bridge this lsquodigital dividersquo

Domestic digital markets are small and fragmented and of insufficient size to attract investment or provide the addressable customer base for technology-driven companies to rapidly scale Basic digital literacy levels remain low and the regionrsquos educational institutions are not producing the volume of workforce-ready computer and network engineers software developers and other high-skilled digital professionals needed subsequently constraining growth and investment by tech and telecom firms in the region Governments are only scratching the surface of the opportunities associated with leveraging technology to improve service delivery

While these challenges are not unique to East Africa the region is uniquely placed to overcome them and to thrive as a leading hub in the global digital economy As the birthplace of some of the most innovative digital solutions in Africa including mobile money the region is well positioned to capitalize on the momentum created to date in moving toward a single digital market (SDM)

1 ITU World Telecommunicationinformation and communication technology (ICT) Indicators database Regional average based on country-level internet penetration weighted by country population

Building on its relatively strong trade financial and political integration protocols and institutions as well as harnessing the dynamism and reputation of its growing tech clusters deeper cross-border integration of digital markets and closer coordination between East African countries could drive the creation of a strong and competitive regional digital ecosystem that is able to attract investment promote innovation and spur job creation

Ultimately the drive for an SDM is about creating economies of scale and capitalizing on the network effects necessary to increase the regionrsquos competitive edge This is particularly important in the context of the digital economy given the high fixed costs of telecom infrastructure and data centers the need for tech start-ups to rapidly scale and capture market share and the exponential growth in value of digital platforms and services as increasing numbers of users adopt the services and generate more data and content

This report aims to outline the case for the creation of an SDM define the key elements that comprise an SDM and inform the approach toward its implementation

ldquo SDM is about creating economies of scale and capitalizing on the network effects necessary to increase the regionrsquos competitive edgerdquo


1 The building blocks of the Single Digital Market

Africa The vision strategic framework and roadmap adopted for achieving an SDM in East Africa thus needs to be anchored in the unique challenges and strengths that characterize regional and domestic markets An assessment of these market structures is presented in Chapter 3 of this report However the overarching strategic framework presented here is likely to be applicable to similar regional integration elsewhere in Africa including the recently launched pan-African initiative under Smart Africa to move toward a Single African Digital Market (see Annex C for more details)

Comprehensive stakeholder consultations revealed a diversity of views of what an SDM would mean for East Africa before ultimately coalescing around the following vision statement (the lsquoSDM Visionrsquo)

The vision articulated is grounded in the regionrsquos aspirations to forge a new path for East Africarsquos socioeconomic development pairing an ambitious regional integration agenda with the desire to unlock the opportunities presented by the spread and development of innovative digital technologies and the growth of the wider global digital economy

11 Leveraging the lessons learned from global SDM initiatives

The SDM initiative for East Africa proposed in this report draws on the successes and lessons learned from similar initiatives implemented elsewhere2 The largest established example to date is the European Digital Single Market which allows individuals and businesses to ldquoseamlessly access and exercise online activities under conditions of fair competition and a high level of consumer and personal data protection irrespective of their nationality or place of residencerdquo It is estimated that accelerating Europersquos digital potential through a single market initiative could add trillions of euros to economic growth in less than a decade3

The European Digital Single Market strategy comprises a wide range of initiatives that help achieve this vision These include but are not limited to (a) achieving universal availability of superfast broadband (30 Megabits per second (Mbps)) across all member states by 2020 (b) eliminating roaming prices (c) harmonizing spectrum licensing regimes (d) ensuring the free flow of data across the region (e) cooperating on issues such as cybersecurity privacy and data protection (f) addressing value added tax (VAT) and geo-blocking issues to make cross-border e-commerce easier and (g) harmonizing laws in areas such as copyright online contracts and consumer protection

12 A vision for a Single Digital Market in East Africa

While there is value in drawing on other SDM initiatives all regions have different market structures and characteristics Solutions that work elsewhere may therefore not be entirely suitable in East

2 Nascent SDM initiatives have also begun in Latin America and Eurasia with similar aims of digital market integration

3 See httpswwwmckinseycombusiness-functionsdigital-mckinseyour-insightsdigital-europe-realizing-the-continents-potential

ldquo A single digital market is one in which cross-border barriers to providing and accessing digital infrastructure content and services are eliminated and one in which a seamless competitive regional digital ecosystem drives a reinforcing cycle of economic growth investment innovation job creation and improved service deliveryrdquo


13 The SDM strategic framework

PROMOTE THE DIGITAL MARKET

Digital IDDigital paymentsE-transactionsConsumer protectionDigital public servicesTrade and customsLogistics

Ensure e-commerce digital services and the functions that support them all work across bordersRemove trade and customs barriers for goods purchased online

Ensure the data protection and privacy laws allow for cross-border data transfersShare cybersecurity resources in the region

Remove cross-border barriers to infrastructure and connectivity (wholesale and retail)

Where relevant create scale for these analogue complements across the region

Data protection and privacyCybersecurityContent regulation

InfrastructureServices

DIG

ITAL

SKIL

LSENABLINGENVIRONMENT

INNO

VATI

ON

ENVI

RONM

ENT

HARD

INFR

Arsquo(E

GP

OW

ER)

DIG

ITAL

LEAD

ERSH

IP

CAPI

TAL

FINA

NCIN

G

REMOVE CROSS-BORDER BARRIERS

SINGLEONLINEMARKET

SINGLE DATAMARKET

SINGLE CONNECTIVITY MARKET

The SDM framework consists of three markets or layers and an enabling environment foundation as detailed in Figure 1 A vibrant SDM will be based on promoting the development of all three markets and the enabling foundation within each country while simultaneously removing cross-border barriers to integration (regionally and in some cases globally) within each layer to create a seamless regional market

Figure 1 Overview of SDM vision and strategic framework

Ultimately each layer of the SDM builds on the other However with the development of each distinct layer a positive feedback loop will emerge which reinforces the expansion and integration of the SDM For example while connectivity is a prerequisite for the development of online services as the online services market expands so will the demand for connectivity infrastructure as access to the internet becomes more attractive to new users


A single connectivity marketTo participate in the digital economy firms citizens and governments must first be online A single connectivity market is one in which the capacity speed and quality of connectivity services needed to support the free flow of data and online service provision is available from anywhere in East Africa at an affordable price This would apply to both the retail-level connectivity services available to end users as well as wholesale connectivity made available to the internet services providers (ISPs) that serve them

Cross-border barriers to both connectivity infrastructure and services deployment and affordability would be removed creating a larger single connectivity market with equal access to infrastructure (for example network coverage) as well as a level playing field for wholesale connectivity services (for example equivalent pricing for international internet protocol (IP)) transit and retail services (for example reducing or eliminating roaming rates) Removing these barriers would help infuse greater competition which would in turn help drive down prices It would also minimize differences in international capacity and prices between countries A single connectivity market would thus have limited or no price or performance differentials for connectivity across the region This feature would be critical for

the landlocked countries in the region which currently face high transmission charges to access international connectivity through their coastal neighbors

At a wholesale level this could be achieved by ensuring interconnection and interoperability of national backbone networks across the region (physical and regulatory)mdashfacilitating the construction of new cross-border networks and streamlining licensing requirements or obligations for regional players At a retail level this could mean extending existing regional roaming initiatives under One Network Area (ONA) to cover data (see Focus Box 1 for more details on the ONA)

The economies of scale created by a single connectivity market would also help improve the affordability of related services and infrastructure deployment By lowering wholesale and retail costs and increasing the availability of connectivity infrastructure across the region a single connectivity market would help bring more people online bridging the current lsquodigital dividersquo both within and between countries in the region

Increasing connectivity through a single connectivity market is a necessary stepping stone for the development of a single data market The only way that data can flow freely across borders is on top of seamless regional networks Equally a larger online population across the region is critical in making the development of a vibrant single data and online market viable

Focus Box 1 ONA - A successful case of regional digital market integration

High roaming charges and interconnectiontermination rates can create significant barriers that stifle cross-border communications In 2014 the countries of the East African Community thus made a joint commitment to fast-track the creation of an ONA The ONA currently covers Kenya Rwanda Uganda and South Sudan through an accelerated integration program under the Northern Corridor Initiative with the possibility of Tanzania and Burundi joining when they are ready The agreement introduced harmonized rate caps for cross-border traffic originating and terminating within participating ONA countries and elimination of roaming surcharges for users travelling within the region The ONA was initially applied to voice services in all four countries and has more recently been extended to data in Kenya Uganda and Rwanda though reports suggest the latter is not yet fully complete

In Uganda retail roaming rates were cut from US$093 to US$010 per minute (based on figures from 2016) following the introduction of the ONA

The impact of lower tariffs on individual roamers has been significant with cross-border voice traffic growing rapidly In Kenya and Uganda cross-border voice traffic has tripled Meanwhile Rwanda and South Sudan both experienced a fivefold and threefold increase respectively in the wake of the ONArsquos introduction As roaming represents a minor source of revenue for mobile network operators (MNOs) the initiative is not seen to have had an adverse impact on industry

The ONA is an example of what can be achieved when target initiatives are prioritized and fast-tracked at the regional level but also the price sensitivity of consumersmdashsuggesting that other interventions that help lower connectivity prices for consumers will stimulate growth in the regional uptake of digital and information and communication technology In other words fully extending the initiative to data would likely help increase cross-border data exchange in the region

Source Kelly T and C Kemei 2016 WDR 2016 Digital Dividends A Case Study of ONA ITU4

4 See httppubdocsworldbankorgen499731452529894303WDR16-BN-One-Network-Area-in-East-Africa-Kelly-Kemeipdf and httpswwwituintdms_pubitu-dopbprefD-PREF-EFONA-2016-PDF-Epdf


Single data market The digital economy is driven by data Globally cross-border data flows have increased some 45 times since 2005 In the past two years alone this cross-border data exchange is estimated to have boosted global gross domestic product (GDP) by 10 percent and is worth some US$78 trillion5 This trend shows no signs of abating6 According to IBM 90 percent of all data that exists today have been created within the past 12 months growing at a rate of 25 quintillion bytes per day7 In sum data information flows now have a more significant impact on GDP growth for countries that trade in traditional goods8 Looking ahead more and more critical infrastructure and new services will be heavily data driven This includes the Internet of Things (IoT) which will involve adding connectivity to many common devices we use every day as well as critical infrastructure such as power networks This offers great potential but needs to be both proactively nurtured as a source of growth and productivity while also carefully managed to mitigate risks

A single data market would unlock the data-driven economy in East Africa allowing data to flow freely and securely across bordersmdashto be processed analyzed stored or used anywhere in the region subject to compliance with appropriate regulation This would require the harmonization of data laws and the creation of explicit terms under which data could be safely stored and exchanged between countries (similar to existing European lsquoSafe Harborrsquo rules) in turn supporting regional deployment and access to data infrastructure and data-driven services and innovation

An East African single data market would encourage the creation of a much larger pool of data which could enable data-driven innovation (DDI) and lsquobig datarsquo analytics resulting in significant economic and social benefits and efficacy gains across virtually all sectors Governments would support the availability of open data from public and potentially also private data sets by adopting regionally (and globally) standardized machine-readable formats that could foster the development of DDI

5 lsquoSingle Digital Market for Africa Reportrsquo Transform Africa Summit May 2018

6 See Data-Driven Development 2018 (forthcoming) Information and Communication for Development World Bank

7 IBM 2016 see httpswww-01ibmcomcommonssicgi-binssialiashtmlfid=WRL12345USEN

8 See httpswwwmckinseycombusiness-functionsdigital-mckinseyour-insightsdigital-globalization-the-new-era-of-global-flows

However robust data protection and privacy laws as well as cybersecurity procedures would need to be in place to protect sensitive data maintain consumer confidence in data-driven services and facilitate data sharing of governmental records For example harmonized regulatory frameworks would need to be established as well as joint platforms to boost regional readiness to prevent and address shared cybersecurity threats and related attacks

In an SDM there would be no undue restriction on content coming into a country from elsewhere in the region thus precluding practices such as geo-blocking Equally intellectual property (IP) rights and content protection would need to be coordinated and enforced

The creation of a larger market would also generate substantial cost savings by creating economies of scale that make investment in regional data centers that support online services including cloud hosting more financially viable However achieving this would necessitate the removal of data localization requirements that confine data storage within national borders

Ensuring the free flow of data is an essential enabler for many online services such as e-commerce digital payments and other cloud services to work across borders These services are built on large-scale data Free-flowing data is also a prerequisite for any regional cooperation on digital ID which is essential for many online transactions A single data market is therefore a critical first step to encouraging the creation of new and innovative data-driven solutions and a vibrant single online market

ldquo A single data market would unlock the data-driven economy in East Africa allowing data to flow freely and securely across bordersmdashto be processed analyzed stored or used anywhere rdquo


Single online marketIn a single online market innovators entrepreneurs and regional companies would offer digitally enabled services and content freely across the region with no barriers to cross-border delivery and access of digital goods or services For countries in the region with more developed markets for online services such as e-commerce this would stimulate growth by enabling their local businesses and entrepreneurs to reach a much larger regional market In all countries in the region citizens and businesses would gain larger access to a wider range of digitally enabled services It would for example increase the reach efficiency and quality of public services by supporting the digitization of government E-government services would also help familiarize consumers with the use of online services and increase the consumer base for these services Regional coordination of related initiatives in areas such as open data would also help stimulate the development of regional services and content

A single online market would involve cross-border collaboration on a series of prerequisite technologies and legal frameworks including (a) digital identification (ID) (b) digital payments (c) e-transaction legislation and (d) consumer protection Customers must be able to go online identify themselves (if needed) sign and pay for goods and services digitally and feel confident that their personal data and consumer interests are being protected

Certain online services such as finance health and travel will require a digital means of identification which would need to be recognized across borders for a regional market to emerge Digital payment platforms would need to be available affordable and interoperable across the region and work globally enabling users in East Africa to pay for international services Harmonized e-transactions legislation would also need to be in place to create legal equivalence between paper-based and electronic transactions enabling digital signatures

For service providers including government agencies and companies a single online market would enable the efficient delivery of goods and services purchased andor delivered online This would be achieved by a more harmonized approach to trade and customs arrangements lower tariffs and the elimination of nontariff barriers

Regional enabling environmentThe three market layers would need to be underpinned by a supportive enabling environment at both the national and regional level which would help promote the development of the regional digital economy A vibrant digital ecosystem requires the development of digital skills a favorable business and innovation environment deployment of hard infrastructure access to capital financing and effective digital leadership and institutions Collaboration between countries can complement and significantly enhance efforts to improve the enabling environment at the national level

Digital literacy and skills would need to be improved to help users have meaningful access to the internet digital information and services (both public and private) as well as create content and even digital businesses The promotion of advanced digital skills would support the development of the digital economy by training the innovators and entrepreneurs who would develop the next generation of digital services and to fill technology-related jobs opening up across all industries Regional collaboration can help create the scale for investments in centers of excellence to develop more advanced skill sets demanded by industry and to support regional innovation

An enabling environment would make it easier to start new digital companies accelerate their growth domestically and regionally and support innovation and technology adoption among established businesses A more supportive domestic and regional business and innovation environment would need to be encouraged which could support the emergence of new and innovative online service providers help entrepreneurs to develop digital content and services that are locally and regionally relevant while creating new jobs in the region Better access to capital as well as the promotion and facilitation of investment would provide support for both start-ups (such as new online service providers) and larger established companies (such as infrastructure providers) A larger portfolio of potential investments and networks of tech incubators and accelerators across the region as well as proactive regional branding and investment promotion would also help make the East African market more attractive to potential investors and venture capital


The technology that drives the SDM requires affordable and reliable access to powermdashbe it as a means of allowing the average consumer to charge their digital devices or for businesses and governments to provide reliable digital services and operate communications networks Equally solid transportation infrastructure would be required to facilitate the logistics services needed to support e-commerce While both power and transport infrastructure are critical enablers of an SDM these deficits would largely need to be tackled outside of the SDM roadmap through parallel investment programs coordinated at the regional level However an SDM can also support innovative means of improving infrastructure and logistics services These include off-grid solar provision enabled by mobile payment technology improved logistics through digital mapping and cargo drone delivery and facilitation of cross-border trade through regionally integrated customs and immigration information systems and regional data exchange and privacy protocols

Finally effective digital leadership and institutions will need to be in place at both the national and regional level and be able to coordinate and harmonize relevant policy regulation and investments in line with the overall SDM strategic framework and roadmap

14 Action and cooperation - toward a lsquoDigital Roadmaprsquo

Based on the vision and strategic framework (detailed earlier) as well as a comprehensive analysis of the readiness of domestic and regional markets (detailed in Chapter 3) a series of priority action areas have been identified These will need to be addressed at both the regional and national level for an SDM to emerge These action areas are presented here as part of a consolidated SDM roadmap A summary of priority actions under the proposed roadmap is presented in Focus Box 2 whereas the full version can be found in Annex A Priority actions have been selected based on expected impact and feasibility

While many actions identified in the roadmap are ambitious none are unachievable The roadmap hopes to capitalize on the

momentum of ongoing regional integration initiatives such as the ONA the East African Community (EAC) Common Market and the African Continental Free Trade Area (AfCFTA) Implementing the recommendations will require coordinated efforts between national governments in collaboration with regional bodies such as the EAC the East Africa Communications Organization (EACO) Northern Corridor Integration Projects (NCIPs) and the Smart Africa Alliance Support from private sector organizations nongovernmental organizations (NGOs) academia and donor partners will likewise be critical to the design and implementation of policy reforms and investment programs A summary of ongoing integration efforts including work undertaken by pertinent regional bodies and fora that are critical to the implementation of the SDM is summarized in Annex C

In addition to working with said institutions and stakeholders coordinating these parallel efforts will be necessary This will ensure clarity on roles and responsibilities avoid duplication support capacity building bridge existing data gaps and allow for effective monitoring of progress Consultations suggest that a designated coordination body might be necessary to facilitate this process This body could also help communicate the vision articulated by the SDM initiative

By taking proactive action and working together rather than in competition the result can be a win-win for all East African countries Most importantly it can help close the lsquodigital dividersquo among East Africarsquos people ensuring that everyone has access to and is benefiting from digital technologies rather than just a privileged few

Existing SDM strategies employ scorecards to track implementation of the overarching strategic framework over time The European strategy is for example linked to measurable targets It defines several quantifiable indicators against which progress can be tracked in each country Indicators are regularly published through the Digital Scoreboard and the Digital Economy and Society Index The scorecard proposed for the SDM in East Africa detailed in Annex B replicates this model but with indicators and targets selected to correspond to the unique East African context and aspirations


Focus Box 2 Digital Roadmap Top priority actions9

SINGLE CONNECTIVITY

MARKET

Action

i Undertake coordinated PPP investments toward achieving universal broadband access across the region (regional backbones and access networkslast mile)





bull Reduce or eliminate VAT for telecommunication services

iii Extend ONA coverage to more countries and services




SINGLE DATA MARKET

Action





9 This table serves as a selection of the top priorities from among the full Digital Roadmap recommendations found in Annex A



Action


ii Establish full interoperability between mobile money networks (domestically and regionally) with a glidepath toward lower exchange fees for cross-platform and cross-border transactions

iii Adopt harmonized e-transactions and consumer protection laws based on EAC Electronic Transactions Act

ENABLING REGIONAL

ENVIRONMENT

Action

i Establish Regional Centers of Excellence for advanced ICT education and research



bull Develop regional investment promotion strategy and marketing campaign

bull Facilitate lsquoupskillingrsquo and networking of national and regional tech hubs incubators and accelerators

bull Cultivate a regional pipeline of start-ups with demonstrated proof of concept ready to attract international venture capitalangel investors

bull Establish a regional tech entrepreneur association to facilitate government-industry dialogue


2 Impetus for the Single Digital Market

ldquo Generating an up to US$26 billion boost in regional GDP and 45 million new jobsrdquo

21 Economic and jobs impactAn East Africa SDM will enable the region to capture a bigger share of the global digital economy driving economic growth and diversification creating jobs and improving services In 2016 the digital economy was worth US$115 trillion or 155 percent of global GDP It is expected to reach 25 percent in less than a decade far outpacing the growth of the lsquotraditionalrsquo economy East African countries are capturing only a tiny fraction of these benefits An SDM offers the opportunity for the region to disrupt its growth trajectory by providing its businesses a more solid footing to compete and empowering its citizens to thrive in the global digital economy Without deeper integration and cooperation East African countries will largely continue to miss out on this lsquo4th industrial revolutionrsquo or lsquoIndustry 40rsquo with only a small elite benefiting while the rest are left behind

An economic assessment undertaken to estimate the impact of implementing an East Africa SDM conservatively projects a boost to regional GDP of between US$093 and US$26 billion over five years (between 057 percent and 160 percent additional growth) driven at first by increased access to the internet with accelerating growth thereafter driven by development of new digital industries and services and adoption of technology and digital platforms by traditional firms It further projects creation of between 16 million and 45 million new jobs (between 22 percent and 62 percent additional growth) over the same period

The analysis also predicts that the impacts will be felt across the income spectrum with those at the bottom of the pyramid gaining disproportionately as citizens and businesses previously shut out of the digital economy due to low incomes and digital literacy are able to connect to the internet and access valuable digital services content and e-commerce platforms for the first time Existing internet users will also capture between a US$12 billion and US$4 billion consumer surplus as the result of falling broadband prices and increased perceived value of being online due to higher quality and variety of digital services and content The full economic impact assessment is available in Annex D


Focus Box 3 Methodology and limitations of Economic Impact Assessment10

The impact assessment used panel data econometric analysis to assess the elasticities of demand for broadband access (that is how sensitive demand is to price network effects and the availability of broadband) The analysis estimated the impact of integration and creation of an SDM in East Africa in two different scenarios Base and Highmdashyielding estimates for the level of expected (a) network effects (b) increase in broadband availability and adoption and (c) reduction in broadband prices These results were in turn used to generate estimates for expected increases in national GDP and jobs for both the Base and High scenarios The model used to estimate job creation leverages ITU research and the impact of increased broadband penetration on job creation Additional econometric analysis using panel data and a two-stage Instrumental Variable (IV) model was conducted to determine broadbandrsquos impact on GDP growth based on a sample divided by broadband penetration levels This model also helped eliminate the risk of endogeneity and was based on a similar study conducted by the World Bank in 2017 In addition consumer surplus benefits to existing usersmdashin terms of both access to a wider network of users (network effects) and a broader range of digital content and servicesmdashwas estimated for both the Base and High scenarios To explore microeconomic impacts detailed survey data from Brazil (which was deemed comparable in terms of geographic and population size as well as its federal structure) was used to predict the general distributional impact of the SDMmdashnotably how expected price decreases for mobile broadband would be distributed across lower income levels through increased adoption A brief thematic case study also explored the expected increase in mobile money access based on an interoperable market under an SDM

Before carrying out the analysis various analytical models were explored (for example input-output and Computable General Equilibrium (GCE) models) Ultimately an econometric analysis method was adopted based on (a) the level of data availability and (b) the analytical groundwork already undertaken in previous World Bank studies The accuracy of this analysis is contingent on the quality and reliability of the data used Ideally the models would have used regionally specific data sets but this was not available Key data points used sourced from the Global System for Mobile Communications Association (GSMA) are subject to some limitationsmdashfor example not all operators in Tanzania and Burundi report figures to the GSMA These figures are thus likely to be underestimated

10 A full account of the methodology used to conduct the economic impacts assessment including results is presented in Annex D

22 Drivers of growth and job creation

At its heart the SDM initiative is about creating the economies of scale and network effects necessary for the East Africa region to be a competitive player in the global digital economy Achieving economies of scale is the primary motivation behind the regionrsquos wider economic and political integration agenda creating a larger regional market to drive competitiveness investment and innovation as well as a critical mass of resources cooperation and attention to address shared challenges and to take advantage of shared opportunities

This is even more essential in the context of the digital economy given the high fixed costs of telecom infrastructure and data centers the need for tech start-ups to rapidly capture market share to fend off rivals and the exponential growth in value of digital platforms and services that more users and data can generate A deeper exploration of economies of scale and network effects at each layer of the SDM framework is included in Focus Box 4

In isolation no country in East Africa has the market size to compete with giants on the continent such as Nigeria much less global digital economy leaders such as the United States China and the European Union (EU) However with a combined population of nearly 180 million an integrated East African digital market would be the ninth largest market in the world (in terms of population) creating the market size needed to both attract digital investment and provide a larger and more familiar space for local firms to grow before having to compete in the global arena However to realize the benefits far more of the 180 million will have to actually be online

Accelerated growth of the digital economy both nationally and regionally would generate benefits for all segments of society For the private sector this would open a larger market for entrepreneurs and small and medium enterprises (SMEs) support the emergence of digital entrepreneurship and provide new sources of investment For citizens this would increase the availability and affordability of connectivity (mobile and internet) relevant digital content and services and create new employment opportunities Lastly an SDM would improve the quality of public services and help governments deliver these more effectively by moving them online


Figure 2 Population size of the largest global markets

1400

1200

1000

800

600

400

200

200 M20

0 M

CH

INA

IND

IA

E27

USA

IND

ON

ESIA

BR

AZI

L

PA

KIS

TAN

NIG

ERIA

EAST

AFR

ICA

EAST AFRICAB

AN

GLA

DES

H

RU

SIA

MEX

ICO

JAP

AN

PH

ILIP

PIN

ES

TANZ

ANIA

KENY

A

UGAN

DA

SOUT

H SU

DAN

RWAN

DA

BURU

NDI

0

200

0

POPU

LATI

ON

(MIL

LLIO

N)

Source World Bank 201811

The SDM will unlock opportunities for accelerating the wider East African integration agendamdashfacilitating the free movement of goods and people across borders through mutual recognition of digital IDs as well as integrated digital platforms and databases for customs immigration and revenue collection The SDM will also help East Africa interact more effectively with the rest of the world for example by removing barriers to international payments or the use of cloud services from other regions

While all countries in East Africa stand to gain from an SDM the benefits derived from the initiative will vary depending on individual countriesrsquo level of digital maturity Countries in the region currently enjoy varying levels of digital maturity (detailed in Chapter 3) Those that are on the lower end of the spectrum of technology adoption and have less dynamic market structures

11 Based on latest population data available from 2016 See httpsdataworldbankorgindicatorSPPOPTOTL

face a risk of increased competition in regional markets by better positioned market playersmdashearly adoptersmdashthat may constrain the development of their indigenous industries However the economic analysis suggests that the less digitally advanced countries will gain greater access to services infrastructure and investment to support the acceleration of their digital development and more than offset these risks The more digitally advanced nations will benefit from a much larger market for digital services and entrepreneurship as well as lower prices and greater choice in the online services offered While any regional integration endeavor comes with considerations of national sovereignty the net gains far outweigh the costs and risks from joining an SDM in terms of the development of domestic markets job creation and the network effect of integration across the economy compared to a scenario of remaining outside of a regional integration effort


Focus Box 4 Unlocking economies of scale and network effects

Economic benefits of scale and network effects can be realized at every layer of the SDM


Connectivity infrastructure such as fiber optic networks as well as connectivity services benefit greatly from scale as the costs of capital investment and operating costs can be spread across more users through infrastructure sharing based on open access Economies of scale from an SDM would enable wholesale and subsequently retail connectivity services prices to fall in the region as operators are able to deploy and operate regional backbone networks at lower unit cost by reaching more customers More affordable prices would in turn help expand access and boost demand for related services generating increased data traffic and online activity critical to the business case for further network investment in capacity upgrades and expansion to new areas Lower cost and more accessible connectivity would also pave the way for more innovative services that rely on higher bandwidth capacity for example incorporating video and interactivity further reinforcing this positive cycle

SINGLE DATA MARKET

Data-based services increasingly require cloud-based data storage solutions East Africa is currently underserved by local data centers and much of the data used and generated in the region is stored and processed overseas A single data market would provide the scale necessary to support investment in more regionally based data centers A single data market would enable cloud-based services to be hosted in one location yet serve the entire region generating considerable cost savings and improving energy efficiency and security Localregional hosting of these services requires expensive investments in data centers as well as ancillary investments in internet exchange points (IXPs) to minimize latency and reduce reliance on international connectivity Both would benefit significantly from the economies of scale and network effects generated by an SDM Top-tier data centers face high initial investment costs with low incremental expansion costs Regional and national IXPs make more sense the more data that is hosted locally and regionally and where they can be co-located with larger data centers The network effects created by a single data market would also help spur investment in data services driving a reinforcing cycle of supply and demand for data-based services and infrastructure


Online services are developed in response to anticipated demand and an SDM would create a much larger potential customer base for local developers of digital services and content Consumers in this larger market would reap corresponding benefits in terms of greater choice and service availability Social media e-commerce platforms and data-driven services all rely on network effects provided by users generating content data and larger pools of buyers and sellers with the value of such platforms and services growing exponentially with each new user A critical barrier to internet adoption for many people in East Africa is the lack of content and services that are relevant to local communities and available in local languages A larger market and compounding network effects would lead to the development of better content and could therefore also help boost uptake of digital services

ENABLING REGIONAL ENVIRONMENT

Programs to promote digital skills innovation and investment can all benefit from increased scale and coordination across the region An SDM would create a larger potential market for entrepreneurs while also potentially increasing access to necessary training and venture capital funds which would be attracted by the greater growth potential afforded by a larger market


3 Assessment of current digital markets

Figure 3 Internet users and broadband penetration rates 2017

O

F PO

PULA

TIO

N B

ASED

ON

SU

BSC

RIPT

ION

S

0

5

10

15

20

25

30

35

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA52 67

13

20219

262 262

92

27

337

34

02 03 0311

82

0 0

Internetusage rates

Mobilebroadband

Fixedbroadband

This section presents the background research that underpins the SDM Vision and Digital Roadmap proposed earlier The assessment of the current state of domestic and regional digital markets including persistent barriers to achieving an SDM presented here is also reflected in the baseline data summarized in the SDM Scorecard (found in Annex B)

31 A single connectivity market

Connectivity markets in East Africa are developing rapidly The ITU reports that there were some 33 million internet users in the region in 2017 with 8 million new internet users being added in 201612 Figures published by national telecom regulators suggest that this figure may be even highermdashcloser to 70 million13

However while access is growing millions of people remain unconnected National markets also diverge widely in their level of development with disparities in access pricing and capacity Substantial connectivity divides persist both between and within countries with widespread inequality in access For example women in East Africa are 45ndash70 percent less likely to be internet users than men based on country-wide surveys carried out by Facebook in Kenya Rwanda and Uganda14 This presents a key challenge as well as an opportunity for regional integration

All countries in East Africa benefit from the presence of private sector telecom operators though some domestic markets are more vibrant and competitive than others These private sector operators have played a major role in expanding network

12 Based on calculations from the ITU World TelecommunicationICT Indicators database 2017

13 Figures from 2016

14 Figures from 2015 Facebook State of Connectivity 2015 Report based on surveys conducted by Facebook over 2014ndash2015 See httpsfbnewsroomusfileswordpresscom201602state-of-connectivity-2015-2016-02-21-finalpdf


coverage Nevertheless the private sectorrsquos investment imperative is often not strong enough to support network expansion in the regionrsquos most remote and poor areas In these areas communities tend to be dispersed raising the cost of deploying networks and services These communities also tend to have much less money to spend on connectivity services creating limited demand for service provision

Access networks which provide connections to end users predominantly rely on wireless technology in East Africa Most current users therefore access the internet through mobile networks and devices Basic 2G mobile network coverage ranges from 53 percent in South Sudan to nearly 100 percent in Rwanda 3G network coverage ranges from 20 percent in South Sudan to 94 percent in Rwanda and 4G coverage remains limited15 (see Figure 4)

Figure 4 2G 3G and 4G mobile coverage based on population 2017

However based on these figures some 37 million people are still out of range of these mobile broadband access networks at the regional level Satellite broadband coverage is universal however the prices charged for related services are out of range for most consumers While the presence of 2G networks facilitates

C

OVE

R BY

PO

PULA

TIO

N

0

20

40

60

80

100

120

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

2G 3G 4G

Source GSMA Intelligence 201716

upgrades to 3G or 4G services as the same infrastructure can often be leveraged upgrading existing regional mobile networks to 3G and beyond remains a major challenge and barrier to increasing the pool of online citizens and achieving an SDM in East Africa1516

Access to internet through high-speed fiber-to-the-premises (FTTP) networks serving business and some residential premises remains limited and concentrated in major cities Kenya leads the way in the rollout of national fiber optic broadband access networks with four FTTP networks (Wananchirsquos Zuku Liquid Telecom Telkom Kenya and AccessKenya) competing in Nairobi and other large cities17

Innovative network and business models have been spearheaded to broaden access to affordable high-speed internet18 One example includes the 2015 launch of a 120-point Wi-Fi hotspot network in Kampalamdasha partnership between Roke Telecom and Google19 However writ large access network availability remains constrained for rural and remote communities

Currently the expansion of access networks is primarily a national issue being addressed through mechanisms such as national broadband plans universal service funds and license obligations All East African governments except South Sudan currently have some form of national broadband policy or strategy in place

Operators require a national license to build infrastructure and offer services Similarly wireless service providers require spectrum licenses which are issued on a national basis (although with some coordination from EACO) Greater coordination among regional players and harmonization of policy licensing and spectrum

15 See httpswwwgsmaintelligencecommarkets - Please note that 2017 2G data point for Burundi South Sudan and Uganda were not published Data present here thus reflects 2016 ITU figures for these countries Moreover not all operators report to GSMA


17 See httpwwwtechweezcom20161028safaricom-pilots-fiber-home-nairobi-aims-zukus-jugular

18 Google for example is experimenting with providing connectivity from a network of balloons through lsquoProject Loonrsquo Facebook is developing solar-powered drones to relay internet signals over long distances and investing in satellite broadband technology and Microsoft is trialling innovative uses of lsquoTV white spacersquo spectrum to provide connectivity in Kenya and Tanzania One ISP partner in Kenya Mawingu is using technology to provide internet access for as little as US$3 per month See httpwwwhope-magcomindexphpcom=newsampoption=readampca=1ampa=3054

19 Facebookrsquos Express Wi-Fi proposition was launched in Kenya in March 2017 with 100 hotspots in Greater Nairobi and further coverage in Mombasa and Kisumu including parts of Tanzania Partnering with Surf and Internet Solutions Kenya Facebook provides a software and analytics stack enabling local entrepreneurs to provide access to fast and low-cost prepaid internet packages See httpsinfointernetorgenblog20170329announcing-the-launch-of-express-wi-fi-by-facebook-in-kenya


allocation in the region could help expand access networks This would for example make it easier for companies trialing new technologies and business models to roll them out across the region

Access networks are dependent on backbone networks to both distribute high-capacity bandwidth across each country as well as channel international connectivity received through regional and global networks Existing backbone networks primarily connect major cities in Kenya Tanzania Uganda and Rwanda Rural areas are thus also underserved by existing backbone network structures A great deal of further investment is required to achieve high-capacity fiber optic backbone routes across East Africa which would also help introduce redundancy that limits the risk of outages and makes cloud-based services a reliable option

Typically the expansion of networks is driven at a national level and is private sector led though some government support may be necessary for coverage of areas where deployment would otherwise be unprofitable (as noted earlier) PPPs are an effective tool that can be leveraged to help fund the development of backbone networks in the region yet these partnerships must be designed in a way that maximizes impact and value-for-money Nevertheless in countries where the government has retained a monopoly of national backbone networks such as Tanzania this model would not be feasible While governments in Burundi and Uganda have also retained ownership of some backbone infrastructure these networks face some private competition Notably a lack of competition in network ownership has ripple effects on the wholesale market prices for broadband (more on this in the following paragraphs)

The vast majority of capacity connecting the region to the rest of the world is currently being supplied through submarine cables landing in Kenya and Tanzania supplemented by small amounts of international capacity provided through cross-border terrestrial cables as well as some satellite broadband capacity In other words only two of the six countries in the region benefit from direct access to international capacity through submarine cables The other four landlocked countries must access international capacity through cross-border terrestrial cables with corresponding markups in pricing This partly helps explain disparities between existing national connectivity markets Up to 90 percent of all internet traffic in East African countries is currently international20 Reducing the cost of international transit would

20 According to the stakeholder consultations conducted

thus generate significant cost savings which could be passed on to end users making services much more affordable21

The private sector is also spearheading the expansion of cross-border fiber links Liquid Telecom has for example deployed an East Africa Fiber Ring that connects five countries in the region and reaches the South Sudanese border22 While these developments are positive they remain isolated and fragmented and proprietary networks are not necessarily interconnected with competing networks Further expansion of cross-border links could help reduce prices particularly for landlocked countries in the region A coordinated regulatory response would also be necessary to facilitate the interconnection of national networks to create seamless regional backbone networks An interconnected network with greater infrastructure sharing and coordination would both decrease the cost of infrastructure deployment and enhance competition that could help drive down prices further A more integrated market would also provide the scale needed to attract greater investment in fiber optic infrastructure which could help increase deployment and access

In addition to the telecom networks themselves digital services rely on supporting infrastructure such as IXPs and data centers that can support the local or regional exchange of internet traffic and origination of content This will become increasingly important as internet usage becomes more bandwidth intensive and sensitive to latency IXPs play an important role in exchanging traffic among ISPs as well as between ISPs and content providers so that local traffic does not have to be exchanged abroad using expensive international connections Meanwhile data centers can be used to cache international content to host local traffic and as nodes of content delivery networks (CDNs) and even IXPs Data center infrastructure thus also plays an important role in decreasing the cost and latency of accessing content

There is currently a lack of supporting infrastructure in most of East Africa A mere six major commercial data centers are located in East Africa (five in Kenya and one in Tanzania) out of some 4124 co-locations reported globally23 The largest is the East Africa Data Center a carrier-neutral facility built by a subsidiary of Liquid Telecom While the demand for regional data centers and

21 Users in landlocked countries in Africa pay on average US$232 more per month for fixed broadband access than those living in coastal areas according to the World Bankrsquos 2016 Digital Dividends report See httpwwwworldbankorgenpublicationwdr2016

22 See httpswwwliquidtelecomcomabout-usnetwork-maphtml

23 We note that this is not a comprehensive list See httpwwwdatacentermapcomdatacentershtml


IXPs is currently limited partly due to the high cost of connectivity the scope and usage of this supporting infrastructure is expected to grow in tandem with increased demand for content This technology would for example be a prerequisite for the development of more advanced digital services

Most backbone networks in East Africa offer wholesale connectivity to telecom operators ISPs and government and business customers Currently wholesale costs are amplified by the high charges associated with international IP transit services especially in landlocked countries required to facilitate cross-border transfers of connectivity from costal landing points (as noted earlier) At present use of international IP transit services generally tends to be high in the region as most of the content being accessed currently originates from outside of the region This would however change with the development of more local content

Moreover if wholesale customers wish to connect to a data center or peer with an IXP in another country they currently face higher charges because they need to purchase capacity from two separate national backbone networks Current licensing arrangements associated with purchasing capacity from backbone networks therefore contribute to limited and high-cost cross-border connectivity

A single connectivity market would aim to minimize any cost differentials in connecting to infrastructure within or across countries in the region It would also seek to harmonize licensing provisions making it easier to access wholesale capacity across borders which would enable new regional wholesale players to emerge that would spur increased competition Limited competition in the wholesale market currently contributes to high wholesale prices Other factors presently affecting wholesale prices include the high cost of power operations and maintenance taxes on both revenues and rights-of-way24 and cost of licensing fees

High wholesale prices directly trickle down to retail prices adversely affecting the affordability of internet services for consumers Bringing down these costs would thus have a positive impact on the retail market Beyond pricing the quality and reliability of connectivity can be an issue across East Africa Service outages are not uncommon in the region which partly stem from a lack of coordination with respect to infrastructure and cross-

24 Rights-of-way charges for deploying fiber can act as a barrier to infrastructure deployment and result in higher prices for services offered over fiber networks In Tanzania for example the Tanzania National Roads Agency quotes rights-of-way charges of US$1000 per kilometre per year

border network deployments The de facto monopoly of the government-owned national ICT backbone in Tanzania including the lack of adequate redundancy and service reliability of the backbone infrastructure have a particularly pernicious impact on wholesale connectivity competition pricing and quality for landlocked countries in the region which rely on networks in either Kenya or Tanzania for access to submarine cables

All countries in East Africa have at least two sizable retail operators However the retail markets in Burundi and Kenya are among the least competitive with a single operator holding a majority market share (see Figure 5) In contrast to its state-owned monopoly wholesale backbone market Tanzaniarsquos retail market is highly competitive with its top three retail operators enjoying a roughly equal market share and three smaller mobile operators fiercely cutting prices to gain a foothold Increased competition in the retail market in the other East African countries could similarly help drive down prices for consumers and also incentivize innovation of new services such as mobile money

Figure 5 Market share of mobile market connections among retail operators

0

10

20

30

40

50

60

70

80

90

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Operator 1

Operator 5

Operator 2

Operator 6

Operator 3

Operator 7

Operator 4

Operator 8



0

10

20

30

40

50

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F G

NI P

C

223

44 44 44

392

141

261

354

10

175

279

500MB 1G

While retail prices have improved significantly in recent years they remain unaffordable for millions in the region especially low-income households at the base of the pyramid A 500 MB mobile broadband package currently costs between 3 percent and 223 percent of gross national income (GNI) per capita The highest costs are found in Burundi followed by South Sudan and the lowest is Tanzania and Kenya (see Figure 6)

Figure 6 Mobile broadband prices as of GNI per capita

However as noted earlier this will take some time where network coverage is limited Moreover it will require widespread uptake of 4G-capable handsets whereas a majority of users currently own only basic phones

The affordability of devices particularly internet-compatible smartphones is a major barrier to connectivity in the region Devices have become more affordable in the last five years due to the introduction of low-cost Chinese brands Jumia in Uganda reports that the average cost of smartphones has fallen by 45 percent between 2014 and 2017 reaching US$9925 and appears to have fallen as low as US$40 (approximately) in some markets Nevertheless taxes on mobile devices such as VAT and customs duties remain high in many parts of the region For example taxes represented 18 percent to 19 percent of the retail price in Tanzania and Uganda in 201626 A brief period of zero VAT on handsets in Kenya in 2009 saw the sales of devices and mobile services soar This also had a positive net impact on government revenues due to higher levels of telecom service tax collected and positive impacts on the overall economic growth rate However this measure adversely affected sales in neighboring markets and triggered cross-border smuggling Lower yet harmonized tax policy could thus prove effective in expanding access to affordable devices

In a single connectivity market it should also be affordable for citizens to use connectivity services when travelling within the region As noted earlier the ONA has eliminated roaming charges for voice calls and SMS messages between several EAC countries Despite the success of this initiative plans to extend it to data SMS and mobile money services have been slow to materialize Kenya Uganda and Rwanda have now adopted the ONA for data but with nonuniversal application Tanzania and Burundi have made commitments to join the ONA for voice but have not yet followed through as of the time of publication

Lowering regional retail prices will be a critical first step toward increasing demand for connectivity creating the user-base necessary for the development of a single data and online service market Demand for connectivity will also rely on there being more attractive online content and services available as well as prospective users having the digital skills to access them These issues are discussed in subsequent sections of this chapter

25 See httpwwwtechjajacomjumias-2017-white-paper-gives-us-details-mobile-internet-penetration-trends-ugandan-online-consumer

26 See httpswwwgsmaintelligencecomresearch201707taxing-mobile-connectivity-in-sub-saharan-africa630

Retail pricing in a competitive environment is predominantly driven by the underlying costs faced by retail service providers including licensing fees network costs wholesale prices and taxes Retail services in a single connectivity market would benefit from the improvements in regional infrastructure and wholesale service market Equally network costs should decrease where upgrades to higher capacity networks such as 4G are made

Source ITU 2017


32 A single data marketThe ability to easily share data across borders be it for customs immigration or e-commerce purposes will be a key prerequisite for developing an SDM For example when making an online e-commerce transaction companies may need to query national digital IDs Data sharing can be facilitated through both the digitization and standardization of data However for data to flow freely there can be no undue restrictions on the storage and processing of data outside national borders

The NCIP has a working group on intergovernmental data sharing Through this initiative Kenya Rwanda and Uganda have reportedly agreed to Harmonize the related regulatory framework enabling data sharing on common terms South Sudan is in the process of developing similar regulation This framework defines both which data can be shared and what protocols must be followed It will for example allow for the integration of national ID and sim card registration databases

Data localization laws requiring data on local citizens to be processed and stored locally can be detrimental to data sharing and thus have a crippling impact on innovation Moreover it can increase the costs of data storage as well as limit choice for users and reduce services available27 Currently no country in East Africa has a data localization law however some sector-specific regulations are enforcing data localization For example MTN in Rwanda was recently fined US$85 million (10 percent of its annual turnover) for maintaining Rwandan customersrsquo in Uganda28 However Rwanda has also introduced a law that obliges government departments to maintain a cache of their data within national borders which is helping reduce costs for international data transport Overall facilitating data portability will be an important element of the SDM which also helps stem data Monopolization Monopolies29

Similarly data content restrictions can prevent data from flowing freely across borders and present a key constraint to an SDM Most countries appear to have similar approaches to content restrictions and filtering However a 2016 lsquoFreedom on the Netrsquo survey which covered Uganda Rwanda and Kenya pointed to the internet being lsquofreersquo in Kenya but only lsquopartly freersquo in Rwanda

27 See httpswww2deloittecomcontentdamDeloittesgDocumentsabout-deloittesea-about-aec-digital-economy-free-flow-of-data-2016pdf A 2016 report by Deloitte outlines the potentially damaging effect of data localization

28 See httpswwwcnbcafricacomnews20170517rwanda-utilities-regulatory-authority-fines-mtn-us-85m-non-compliance

29 See Single Digital Market for Africa Report Transform Africa Summit May 2018

and Uganda30 Hence some content hosted in Kenya may not be legal in other countries in the region Recently announced regulations in Tanzania require a US$930 license fee for bloggers putting the practice out of reach for most citizens if they operate in compliance with the law31 For a regional content ecosystem to thrive it is important to ensure legal clarity for those developing transmitting or hosting local content and to enable rather than suppress such content development This is equally important in terms of encouraging investment from multinational content companies such as Google Companies wishing to host third-party content in the region will also require protection against intermediary liability For example carrier-neutral data centers should be able to host content without being held liable for how third parties use their services32

Moreover to promote the free flow of data across the region companies need to be assured that intellectual property is protected both domestically and across borders through the development of a regional intellectual property environment This will also encourage more businesses to offer services across the region Initially this would mean that intellectual property registered in one country would be recognized and enforced across the region In the longer term a single regional registration process could be defined and aligned with international agreements relating to the registration and enforcement of patents trademarks and copyright A regional body that manages registration and enforcement could also help facilitate this

30 Freedom House 2016Freedom on the Net Report at httpsfreedomhouseorgreportfreedom-netfreedom-net-2016 While the other countries were not covered in this survey the Freedom House Freedom of the Press report states that the press is either lsquopartly freersquo or lsquonot freersquo These restrictions may extend to online content as well See httpsfreedomhouseorgreportfreedom-pressfreedom-press-2017

31 See httpswwwftcomcontent36098722-4623-11e8-8ae9-4b5ddcca99b3

32 See httpswwwinternetsocietyorgdocpromoting-content-africa

ldquo Data sharing can be facilitated through both the digitization and standardization of data


Figure 7 Date of entry into force for a selection of agreements and treaties regulating the international protection of intellectual property

purchases35 Harmonized data protection and privacy procedures can also avoid a ldquorace to the bottomrdquo in terms of standards in a bid to attract data-driven services at the expense of consumer protection36

Data protection and privacy laws are designed to regulate the collection transmission storage and usage of personal data However only 40 percent of countries on the continent have implemented comprehensive laws on the issuemdashnone in East Africa Bills have been proposed in both Kenya (2013) and Uganda (2015) however they diverge widely and are considered inadequate in terms of supporting personal data storage and cross-border data transfer37 There is thus scope to improve national

35 2016 Centre for International Governance Innovation (CIGI) and Ipsos Global Survey on Internet Security and Trust at httpswwwcigionlineorginternet-survey-2016 Global Survey on Internet Security and Trust The CIGI-Ipsos survey polled over 24000 users in 24 countries about their attitudes on privacy and security When asked about how concerns regarding online privacy have changed their online behavior 47 percent of respondents in Kenya reported that they made fewer financial transactions online than they used to and 44 percent were making fewer online purchases


37 See httpunctadorgenPagesDTLSTI_and_ICTsICT4D-LegislationeCom-Data-Protection-Lawsaspx

Year entering into force

Paris Convention

Berne Convention

Patent Cooperation Treaty

Madrid Protocol

WIPO Copyright Treaty

Burundi 1977 2016 ucirc ucirc 2016

Kenya 1965 1993 1994 1998 ucirc (signed only)

Rwanda 1984 1984 2011 2013 ucirc

South Sudan ucirc ucirc ucirc ucirc ucirc

Uganda 1965 ucirc 1995 ucirc ucirc

Tanzania 1963 1994 1999 ucirc ucirc

Number of contracting countries

177 176 152 101 96

Subject matter International protection of patent and trademark rights

International protection of copyright

Simultaneous filing of international patents

International registration of trademarks

Copyright protection in the digital environment

Source World Intellectual Property Organization 201833 Note WIPO = World Intellectual Property Organization

Countries in Africa face challenges relating to registering intellectual property as the registration process tends to be complex Enforcement of intellectual property protection rules when infringements occur is also not always effective Compared to other countries in Africa Kenya appears to have a fairly advanced intellectual property environment with dedicated bodies frameworks and enforcement agencies to manage cases of intellectual property infringement and a new online copyright registration system However many weaknesses remain including gaps in digital copyright protection Enforcement bodies also tend to lack resources which limits their efficacy34

While data needs to be stored and processed across borders it also needs to be done safely A common approach to data protection and privacy is needed for there to be trust in online services and sufficient safeguards in place to protect all consumers Survey data from Kenya suggests that privacy concerns may be deterring the public from making online financial transactions and

33 See httpwwwwipointtreatiesensummaryjsp

34 See httpwwwtheglobalipcentercomwp-contentuploads201702GIPC_IP_Index_2017_Reportpdf


policy in this area and develop a coordinated regional approach that is grounded in best practice and adopts common standards

The EAC introduced a legal framework for cybersecurity published in 2008 which includes provision on privacy and data protection However these legal provisions have not been fully adopted A single EAC law on data protection based on the Convention on Cybercrime and Personal Data Protection of the African Union (AU) (adopted in 2014) could be an effective means of improving and harmonizing existing regulation38 Guidelines were recently published to aid effective implementation As of July 2018 eleven countries have signed and three have ratified the AU Convention though none from East Africa39 Related provision ought to be swiftly ratified by countries in the region It may also be prudent to align regional regulation with standards applied in other major markets to facilitate global data exchange Examples include the EUlsquos General Data Protection Regulation (GDPR) and Network and Information Security Directive (NISD) introduced in 2018 which precludes data exchange outside the EU with counterparts that do not meet comparable data protection standards40

Consideration must be given to data protection in the public sector as well as the private sector Government agencies are increasingly collecting and digitally storing large amounts of citizen data through initiatives such as national ID schemes This data needs to be properly protected and shielded from abuse Rwandarsquos data collection policy has for example been criticized for not adequality protecting citizensrsquo data41

Conversely increased availability of open data that anyone can access use and share is helping spur service delivery improvements and increase the transparency of public agencies Open data is also being leveraged to provide data-driven insight that supports innovation across a wide range of sectors which is in turn helping address various socioeconomic issuesmdashexamples include enterprises such as aWhere42 and Medafrica43 which are using open data to help smallholder farmers increase their yields or improve access to health information Open data initiatives should therefore be encouraged based on best practice addressing 38 See httpunctadorgenPublicationsLibrarydtlstict2016d1_enpdf

39 See httpsauintsitesdefaultfilestreaties29560-sl-african_union_convention_on_cyber_security_and_personal_data_protection_1pdf

40 See httpseugdprorgthe-regulation

41 See httpsprivacyinternationalorgadvocacy-briefing771right-privacy-rwanda

42 See wwwawherecom

43 See wwwshimbamobilecom

any regional roadblocks to related initiatives The role and availability of open data at a regional level could be compounded by standardizing approaches to data collection formatting and publication through harmonized data classification policy Regional best practice is currently being supported by the African Development Bank

Meanwhile the threat of cybercrime in the region is growing rapidly Both governments and businesses have reported related incidents and data breaches44 The full scale of the problem is unknown partly due to inadequate detection capabilities While digitization offers promising opportunities it can also create vulnerability This phenomenon also poses challenges toward regional integration as some governments may perceive an SDM to result in less national-level control and protection in cybersecurity Yet it also presents an opportunity to maximize resources and capacity in fighting cybercrime

Cybersecurity laws have been passed in Kenya Rwanda South Sudan and Uganda Draft legislation also exists in the other EAC countries Most are based on the 2008 EAC framework noted earlier which seeks to harmonize cyber laws regionally Member states of the NCIP have also signed a memorandum of understanding on cybersecurity dating from 2014 which provides a collaborative platform for jointly tackling cybercrime It calls for the establishment of a regional incident response team and includes provisions on information sharing and collectively developing the technical capabilities needed to detect prevent and respond to cybersecurity threats45

Cyber laws need to be regularly reviewed and updated given the constantly evolving nature of threats It is in all countriesrsquo interests to support a coordinated regional response to cybersecurity building on these provisions and supporting neighboring countries where required Pooling resources is not only cost-effective as in an integrated data market any one country is only as protected as its weakest link As more and more digital services evolve the threat will also intensify with potentially detrimental effects on regional e-commerce and e-health services or critical infrastructure such as power grids financial and customs systems and digital ID schemes that rely on data and digital services

44 See httpswwwiafrikancom20161019a-burundian-hacker-was-able-to-get-the-details-of-over-500-000-kcb-kenya-customers-through-a-vulnerability-in-the-banks-app

45 See httpswwwnciprojectsorgprojectict-infrastructure


33 Single online market Online services cover a wide range of online activities including digital education and health services access to information news and entertainment online shopping and social networking The online service ecosystem in East Africa is growing quickly bringing wide-ranging social and economic benefits for citizens and businesses Many new local e-commerce companies are emerging such as Ugandan Dondolo 2fumbe and Masikini46 or Rwandan Yubeyi Online Shopping and GroceWheels47 Roughly 7 percent of Kenyans were using e-commerce services in 2015 generating approximately US$47 million in revenue48 While Kenya leads the way in e-commerce related transactions only accounted for roughly 5 percent of all transactions made suggesting that there is much more room for growth in this sector49

A majority of e-services currently offered in East Africa are not home grown and still originate outside of the region Notably most popular e-commerce websites are also registered with an international not a local domain name50 A number of websites also operate across multiple countries in East Africa but with local domains registered in each country suggesting that these sites are tailored to each country rather than a wider regional market

Although some online services operate across East Africa they face many cross-border trade barriers which are reflected in both their front-end (for example websites) and back-end (for example warehousing and logistics) operations This is a key binding constraint to an emergence of an SDM

A good example of this is Jumia a pan-African online e-commerce platform selling local and international goods founded in Nigeria Jumia now operates in some 23 countries including Kenya Burundi Rwanda Tanzania and Uganda Despite its regional footprint Jumia operates separate websites warehouse systems and delivery teams in each country and each website only offers domestic shipping51 Large foreign firms with easy

46 See httpswwwexportgovarticleid=Uganda-eCommerce

47 See httpwwwnewtimescorwsectionread228485

48 See httpswwwoxfordbusinessgroupcomoverviewbright-outlook-improving-economic-conditions-and-changing-consumer-preferences-point-strong-years

49 See httpwwwmonitorcougBusinessProsperUganda-advised-consider-e-commerce688616-4329744-tww6d0indexhtml

50 Ranking based on visits over a three-month period from visitors with the Alexa toolbar installed While it is true that a local company could also use com the national domain is typically used for sites serving the national market

51 See httpwwwnewyorkercombusinesscurrencye-commerces-african-challenge-selling-to-people-who-arent-online httpjumiaprco99764-africa-s-leading-e-commerce-site-jumia-has-now-launched-in-senegal

access to capital are able to bear these costs and launch across multiple countries and grow their market share whereas smaller regional start-ups may not have the access to capital time or expertise to do so putting them at a distinct disadvantage and blunting competition

Digital identification is an important enabler of many online services that require providers to know their customer significantly reducing the risk of fraud and increasing trust and efficiency on both sides of the transactions Digital IDs can for example support trade digital payment platforms e-government services financial inclusion and access to health education and social protection programs Yet an estimated 71 million people in the EAC are unregistered corresponding to 37 percent of the total regional population preventing them from obtaining an official means of identification However the number of unregistered people improves somewhat over the mandated age-limit for enrollment in voting or national ID schemesmdashexcluding 161 million people in the region52 In Rwanda everyone over 16 years is said to be registered whereas in South Sudan 437 percent of the population over the age of 18 is unregistered53

Figure 8 Share of population that is unregistered

Source World Bank Identification for Development (ID4D) data set 2018

52 Referred to in the ID4D data set as the lsquocut off-agersquo age 16 in Rwanda and Burundi and age 18 in South Sudan Uganda Tanzania and Kenya

53 Calculation based on the World Ban ID4D data set 2018mdashplease note that the year data was collected varies

0

20

40

60

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Total Population

Populationabove cut-o

O

F PE

OPL

E TH

AT A

RE U

NRE

GIS

TERE

D

53

44

27

49

24

47

19 20 18

40

30


To date all six countries (at least partially) use digital IDs or are in the process of developing them These digital ID schemes typically include biometric data such as fingerprints and iris scans used to prevent fraud and duplication and relevant data is stored digitally on a national database within each country Where digital ID initiatives are being deployed in East Africa they remain primarily focused on national-level solutions with at least some corresponding barriers to cross-border use

East Africa is the only region in Africa that currently allows for any level of reciprocal recognition of IDs albeit for physical rather than digital IDs in relation to travel within the EAC The World Bankrsquos ID4D initiative has an ongoing program in East Africa to help accelerate regional integration of IDs and associated databases supporting mutual recognition and further interoperability54 Greater integration of other databases which leverage national ID numbers could be used to check consumersrsquo credit ratings improving the provision of financial services across the region A harmonized regional approach to IDs would thus also help support the development of a regional digital payments ecosystem

Until recently East Africa has predominantly been a cash economy The region has seen low penetration of credit cards and bank accounts associated with traditional banking The mobile money revolution initially spurred by M-PESA but now offered by many providers across the region has changed this dramatically and is enabling a new wave of financial inclusion that has now spread beyond the region and globally55 Mobile money is also seen as having a positive impact in helping close the gender gap in both access to finance56 and supporting access to economic opportunities57 Most East African countries have significantly more mobile money accounts users than account holders with other financial institutions The exceptions are South Sudan where mobile money is not yet licensed and Rwanda58

54 World Bank ID4D documentation 2017

55 See The Global Findex Database 2017 Measuring Financial Inclusion and the Fintech Revolution

56 There is no large gender gap in access to mobile money in Kenya and Uganda See Findex data 2017

57 A study in Kenya found that access to mobile money services enabled women-headed households to increase their savings by over 20 percent allowed 185000 women to develop business or retail activities reducing extreme poverty among women-headed households by 22 percent See Suri Tavneet and William Jack 2016 ldquoThe Long-Run Poverty and Gender Impacts of Mobile Moneyrdquo Science 354 (6317) 1288ndash92

58 See httpswwwgsmacommobilefordevelopmentprogrammemobile-moneyglobal-adoption-mobile-money-2015-look-data_ftnref1

Figure 9 Penetration of financial institution accounts versus mobile money accounts

Source Findex 2017

Note The latest Findex survey data from Burundi was collected in 2014 Mobile money adoption in Burundi is expected to be much higher than figures indicated here as it is known to have grown in recent years

While mobile money is popular few online services provide direct online mobile money payment capability Nevertheless digital payments are growing quickly in popularity within some marketsmdashusage of digital payments among adults to send or receive money in Uganda is 55 percent and 79 percent in Kenya but remains limited in South Sudan and Burundi59 Moreover there are several barriers to making cross-border payments including network interoperability and high transaction costs which in turn restrict the development of regional online services and the growth of e-commerce (for which they are a prerequisite)

Mobile payment systems in East Africa are mainly domestic limited to a single operatorrsquos network These systems typically have limited interoperability with other domestic operators let alone other regional and international operators60 Kenyarsquos Safaricom is one of the few operators to have enabled international money transfers Registered M-PESA subscribers can send money to Vodacom Tanzania MTN Uganda and MTN Rwanda subscribers Transfers are received in local currency with conversion rates and fees advertised in a text message before money is transferred


60 International e-commerce in Africa The Way Forward International Trade Centre 2015

0

20

40

60

80

SOU

THSU

DAN

BURU

NDI

TAN

ZAN

IA

UG

ANDA

KEN

YA

RWAN

DA

Financial institution account

Mobile money account

O

F AD

ULT

S O

VER

THE

AGE

OF

15

7

56

73

37

31

9

21

3933

51

1


Figure 10 Mobile payment systems deployed in the EAC

System Burundi Kenya Rwanda South Sudan Tanzania Uganda

Africell Money uuml

Airtel Money uuml uuml uuml

BK mVISA uuml

Dau-Pesa uuml

EcoKash uuml

EzeeMoney uuml

ezyPesa uuml

Leo Manoti uuml

M-PESA uuml

mHose uuml

M-Sente uuml

mCash uuml

Micropay uuml

MobiCash uuml uuml uuml

MTN Mobrsquo Money uuml uuml

Orange Money uuml

PAYG Platform uuml

Tangaza Pesa uuml

Tigo Cash uuml

Tigo Pesa uuml

Vodacom M-pesa uuml

Source GSMA Mobile Money Deployment Tracker 201761

In other words no interoperable mobile payment system has yet been deployed across the entirety of East Africa Equally important the transaction fees for doing so remain very high (both between domestic platforms and across borders between platforms) suppressing transactions even where technically available Greater interoperability as well as lower cross-platform and cross-border transaction fees could foster a regional digital payment ecosystem The World Bank is helping develop supporting ICT infrastructure that links regional stock exchanges across the EAC as well as settlement and depository facilities which could facilitate interoperability62 While this process will

61 Data excludes those firms that are not GSMA members (for example Lumicash in Burundi Halotel in Tanzania and so on)

62 See lsquoFinancial Sector Development and Regionalization Project Irsquo World Bank September 2016

first and foremost be a technical one policy makers can also support the process by mandating interoperability The NCIPrsquos ONA roaming initiative offers a model and platform for doing so Related proposals to eliminate roaming charges for mobile money could also include targeted tax reductions Kenya Tanzania and Uganda currently all apply a 10 percent tax on mobile money transaction fees63

Higher transaction fees for cross-border payments can also be a deterrent to making international transfers For M-PESA customers to send K Sh 1000 (~US$10) within Kenya they incur a cost of K Sh 15 (15 percent) whereas the equivalent international

63 See httpswwwgsmacommobilefordevelopmentwp-contentuploads201607Digital-Inclusion-and-Mobile-Sector-Taxation-2016pdf


transaction costs K Sh 33 (33 percent)64 The cost of transaction is also affected by the high currency exchange rates applied in East Africa which for example inhibit small e-commerce transactions where the extra costs can appear prohibitive

Central banks in East Africa have not historically operated reciprocal accounts which means that most transactions must be exchanged from the local currency into US dollars and then into the new local currency Payments thus incur two sets of conversion charges Steps are being taken to facilitate international transfers The Regional Payment and Settlement System (REPSS) was introduced in 2012 with the aim of facilitating payments between 19 member states in the Common Market for Eastern and Southern Africa (COMESA) region This initiative includes Uganda Rwanda and Kenya Burundi also aims to implement the initiative The REPSS provides a single gateway for the settlement of international transactions in euros or US dollars with the Bank of Mauritius providing reconciliation services65 Moreover the East Africa Payment System (EAPS) established in 2014 is enabling the central banks in Kenya Uganda Rwanda and Tanzania to freely interchange local currencies The EAPS links the real-time gross settlement system (RTGS) of the central banks in the four countries and includes reciprocal accounts between the banks that allow for transfers in local currency66

The EAC has also articulated plans for a monetary union and aims to adopt a single currency for East Africa by 2024 These plans also feature the harmonization of financial systems accounting and reporting practices and the establishment of an East African Central Bank67 The World Bank is currently helping support greater interoperability of capital markets If successful these efforts will remove the necessity for regional currency exchange and associated charges In the meantime current efforts to lower the cost of currency exchange rates between countries in the region ought to be fully adopted and implemented

International financial restrictions are also limiting some payments to the region which can have ripple effects on local digital entrepreneurship For example Google Play allows app developers to upload apps and sell them anywhere in the world However currently local developers are unable to receive payments for new apps uploaded on this platform which reduces

64 See httpswwwsafaricomcokefaqsfaq279

65 See httpswwwcentralbankgokenational-payments-system

66 Burundi and South Sudan are yet to develop RTGS systems See httpwwwtheeastafricancokenewsEAC--is-a-single-currency-on-the-way-2558-3323198-view-printVersion-89y0bbzindexhtml

67 See httpwwweacintintegration-pillarsmonetary-union

their incentive to develop local content68 Regional payment platforms thus need to be interconnected with global platforms such as PayPal A large single market can create the business incentive needed for global payments platforms to facilitate greater international financial inclusion

A prerequisite for conducting commercial transactions online is the presence of e-transaction laws that recognize the legal equivalence between paper-based and electronic forms of exchange Equivalence laws have been passed in Kenya Rwanda Tanzania and Uganda and draft legislation exists in Burundi The laws have been developed under the EACrsquos Legal Framework for Cyberlaws69 In October 2015 the East African Legislative Assembly also passed an EAC Electronic Transactions Bill which sought to develop a safe secure and effective environment for consumers businesses and governments to conduct and use electronic transactions It sets regional standards in relation to electronic signatures e-government services consumer protection and the limitation of liability of service providers70 Alignment of national laws with the EAC bill is mixed with some deviations in each country and lack of sufficient regional harmonizationperspective in many

In addition to the cost and practicalities of making digital payments consumer familiarity and trust in digital payment systems is a major barrier to greater adoption This issue is more pronounced for cross-border payments as the potential recourse for fraud or failed transactions becomes more complicated and costly E-transactions laws can help formalize the payment process However consumer protection legislation relating to e-commerce is an important factor in developing consumer trust in e-commerce services In East Africa Kenya Rwanda and Uganda have online consumer protection laws in place with draft legislation in place in Burundi and Tanzania71 However these laws need to be harmonized across borders including reciprocal agreements to recognize and enforce each otherrsquos approaches Countries in the region are currently committed to doing so under the EAC Electronic Transactions Bill yet incur no penalties for noncompliance72

68 See httpswwwinternetsocietyorgresourcesdoc2015discussion-paper-the-mobile-app-divide The reasons for this lack of international payments is relatively opaque

69 See httpunctadorgenPagesDTLSTI_and_ICTsICT4D-LegislationeCom-Transactions-Lawsaspx

70 See httpwwwealaorgnewindexphpmedia-centrepress-releases873-region-set-to-realize-e-business-growth-as-eala-enacts-eac-electronic-transactions-bill

71 See httpunctadorgenPagesDTLSTI_and_ICTsICT4D-LegislationeCom-Consumer-Protection-Lawsaspx

72 See httpwwwcofekcokeEAC20Electronic20Transactions20Bill202014pdf


People in East Africa are used to face-to-face transactions However boosting consumer trust in e-payments and virtual transactions will be critical to supporting the development of a single online market This may for example require government initiatives that encourage citizens to make digital transactions for the first time through the introduction of digital public services Digitizing both internal and external functions can also increase government efficiency and improve the quality and reach of public services As noted earlier the digitization of government functions and data can also facilitate data sharing with the adoption of regional standards for ICT hardware and software that ensure interoperability wherever possible Moreover it will make it easier for citizens and businesses to interact with relevant public services across the region by making public authorities more accessible online and increasing the reach of both public services as well as online services more broadly

Most countries in East Africa still have a long way to go in terms of developing their e-government capabilities (see Figure 11) However regional coordination can accelerate the proliferation of digital public services through the sharing of ideas and platforms

Figure 11 E-Government Development Index scores (measured on scale from 0 to 1)

Source United Nations 201673

73 The E-Government Development Index is calculated based on the proportion of central government employees and organizations making regular use of computers the internet local area networks and intranets and making their services available to citizens online Latest scores available are from 2016 See httpspublicadministrationunorgegovkben-usreportsun-e-government-survey-2016

0102030405060708090

100

UG

ANDA

BURU

NDI

TAN

ZAN

IA

KEN

YA

RWAN

DA2016

90 85 93 91 91

6979

6982

972014

0

02

04

06

08

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

AFRI

CA

EURO

PE

KENY

A

RWAN

DA

023018

034 034029

072

036042

However businesses also have a significant role to play in terms of boosting consumer confidence in the safety of online services by integrating service functions that build trust as part of their business model This means enabling easy and transparent returns providing customer ratings and review systems and escrow accounts so that payments are not taken until goods are received An SDM would allow e-commerce enterprises that deliver good customer services to expand and disseminate best practices across the region

For e-commerce to thrive in the region goods and services will need to be delivered seamlessly across the region at scale This will require the gradual harmonization and streamlining of regional trade and customs policies facilitating cross-border shipments of physical goods The EAC has already adopted a Common Market Protocol (CMP) that came into force in 2010 which promotes the free movement of goods within the region by eliminating tariff and nontariff barriers74 The Community Customs Management (Amendment) Bill75 and the EAC Elimination of Non-Tariff Barriers Bill76 both introduced in 2015 also support this objective

Figure 12 Good country scores

Source Common Market Scorecard 201677

74 See httpswwweacintcommon-market

75 See httpwwwealaorgdocumentsviewthe-east-african-community-customs-managment-amendment-bill2015

76 Ibid

77 Scored 0 to 100 with 100 being the best These are composite scores depicting performance against customs and trade liberalization provisions agreed under the EAC CMP In other words they reflect the removal of trade and customs barriers facilitating cross-border movement of goods Latest scores were compiled in 2016 See httpsd3n8a8pro7vhmxcloudfrontneteatradehubpages2893attachmentsoriginal1481012380East-Africa-Common-Market-Scorecard-2016pdf1481012380


However while tariffs on intra-regional trade have been formally eliminated by parties to the CMP measures with equivalent effect remain including an upsurge in nontariff barriers78 These would need to be removed for an SDM to emerge and to increase regional trade with the rest of the world

The NCIP has also been taking steps to create a Single Customs Territory using digital technology to reduce customs delays and improve the free movement of goods Objectives include migrating physical controls onto an electronic clearance processing system and harmonizing the implementation of a Common Electronic Cargo Tracking System79 The digitization and standardization of customs and revenue authoritiesrsquo information systems has proved to be the critical first step which again suggests that e-government and the digitization of public service delivery is an important building block for the development of an SDM80 Meanwhile the AU recently announced the launch of the African Continental Free Trade Area (AfCFTA) which will seek to create a single market for goods and services with free movement of business persons and investments across the continentmdashwhich could boost intra-African trade by up to 52 percent by 2022mdashby removing tariff and nontariff barriers81 While these developments are very encouraging and significant progress has been made further efforts in this field are needed to remove cross-border barriers to trade (both digital and analog)

Cross-border deliveries of consumer goods purchased through online e-commerce services also require robust and integrated logistics systems able to deliver goods to and from the border and on to customer premises or shared local delivery points These systems are currently weak due to the trade and customs barriers detailed earlier and the absence of companies that ship goods across borders However the lack of formal physical address systems outside of major cities is also a major barrier82

78 See httpdocumentsworldbankorgcurateden799871468194049251pdf938430WP0v20Bo0arket0Scorecard02014pdf

79 See httpswwwnciprojectsorgprojectsingle-customs-territory

80 Based on stakeholder consultation in Kenya

81 See httpswwwaljazeeracomnews201803african-continental-free-trade-area-afcfta-180317191954318html

82 According to stakeholder feedback

Several initiatives are trying to address this challenge including Kenyan start-up OkHi Founded in 2014 OkHirsquos aim is to develop a physical address system for billions of people globally that are without a physical address by providing a lsquotagrsquo for each house comprising GPS coordinates and a picture of the house83

Poor logistics has been a challenge for e-commerce firms currently operating in the region and is seen as a barrier to those who might otherwise be interested in investing However the scale offered by an SDM would enable firms to establish regional distribution hubs reducing the cost and time of deliveries thus creating a much more effective and vibrant e-commerce market and more attractive investment opportunities This would also reduce reliance on intermediaries such as EasyBuy which place orders with international e-commerce companies and then make deliveries to customers within their country often in exchange for cash-on-delivery to overcome payment barriers84 Regional scale and seamless logistics provided through the SDM would also benefit many players in the logistics market including distribution companies such as Sokowatch who are expanding their operations in the region85

Innovative digital delivery solutions can also help overcome some of the persistent challenges associated with poor transport and distribution networks Unmanned drones are being piloted by both the private and public sector to increase access and delivery of good and services The Rwandan government is for instance trialing drones to support the distribution of medical supplies to remote areas Global e-commerce giants such as Amazon are also exploring drones as options for parcel delivery as are regional firms such as Mobisol86

Leveraging the regionrsquos appetite for innovationmdasha track record for embracing challenges and turning them into opportunitiesmdashgives the region a comparative edge which could be amplified by the creation of an SDM and supported by coordinating regional policy on emerging technology

83 See httpdisrupt-africacom201712kenyan-addressing-startup-okhi-hits-100k-deliveries

84 See httpswwweasybuyafricacom

85 See httpdisrupt-africacom201705kenyan-merchant-solution-sokowatch-starts-east-african-expansion

86 See httpwwwdwcomenin-africa-get-your-next-delivery-by-dronea-19296585


0

50

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F PO

PULA

TIO

N76 89

328

56

294 267

consumers to charge devices such as smartphones Across East Africa 118 million people still lack access to electricity88mdasha mere 76 percent have access to energy in Burundi (as shown in Figure 13)

Figure 13 Access to electricity 2016


In other words many more citizens in East Africa have access to mobile networks than to the electricity needed to charge their devices However digital platforms and solutions can be leveraged to expand access to power Mobile-money-enabled off-grid alternatives such as M-KOPA Mobisol and BBOXX are distributing household solar power solutions to over 1 million homes across Kenya Tanzania and Uganda90 Some of these companies have even started adding mobile connectivity and data plans to their solar power bundles Yet their market expansion is hampered by the limited interoperability of existing digital payment systems and the absence of harmonized data privacy legislation (noted earlier) illustrating the need to fully implement a single data and online market Though not an lsquoonline servicersquo continuing to leverage existing synergies between expanded access to ICT and energy will be critical to accelerating progress toward an SDM Investment in telecom infrastructure can for example benefit from coordination with utility networks Kenya Power has for instance helped deploy over 4000 km of fiber optic cable along its transmission lines91

88 Based on calculations from httpstrackingsdg7esmaporg

89 See httpsdataworldbankorgindicatorSPPOPTOTLlocations=ID or httpstrackingsdg7esmaporg

90 See httpsolarm-kopacomabout


34 Key enablersMany enabling factors contribute to the development and success of critical attributes of the market layers detailed earlier For example the expansion of connectivity will be contingent on sustainable energy provision Equally for IT start-ups to flourish they need both the human capital to sustain them and a business environment that is conducive to growth Some enablers that are specific to the SDM such as bridgingremedyingfixing the digital skills deficit will be directly addressed through the Digital Roadmap However not all enablers can be addressed as part of the road map proposed as they are often broader in nature and concern a much wider audience of stakeholders These areas will nevertheless benefit from coordination and parallel efforts to address themmdashhere the SDM roadmap provides a further platform for advocating for concerted action in these key areas

For example improving transport infrastructure is not just critical to economic development but an important aspect of the overall SDM Vision particularly in the context of enabling logistics for e-commerce development Though addressing major transport infrastructure deficits is beyond the direct scope of the Digital Roadmap close coordination with the transportation sectors is encouraged not least in view of the potential efficiency gains to be made (for example by coordination network deployment with roadworks) Considerable cost savings of up to 70 percent to 90 percent are possible when fiber is installed alongside other infrastructure87 Many projects to improve highway and railway infrastructure are under way in the region The Northern Corridor program is for example helping to fast-track the development of a standard-gauge railway linking Rwanda and Uganda to the port of Mombasa These projects will not only help facilitate regional goods trade but also provide significant opportunities for encouraging cross-border fiber network links at low additional cost

Similarly there are synergies between access to energy and connectivity The availability of power is a significant barrier to the development of the digital economy in East Africa The poor coverage and reliability of existing power networks increases reliance on alternative power sources such as diesel generators which in turn drive up the operational costs associated with running telecom networks and data centers The low coverage of grid power also makes it more difficult and expensive for

87 See httpwwwworldbankorgentopicictbriefmaking-rural-broadband-affordable


A conducive regional and national business environment is critical for the creation of digital companiesmdashenabling innovative enterprise to grow locally regionally and ultimately globallymdashbut also to encourage innovation among established businesses Deployment of connectivity infrastructure and the development of data-driven and online services will first and foremost be private sector driven While movement toward an SDM will create a much larger regional market that benefits entrepreneursmdashby expanding the size of the possible customer base and helping highlight success storiesmdashconcerted action at both the national and regional level is needed to implement policies that encourage innovation and increase access to capital

The existing business regulatory environment is hampering the ease of doing business in East Africa and thereby affecting the creation of a potential SDM In Uganda for example there are reportedly 13 separate procedures required to start a business92 Most East African countries rank poorly in a corresponding World Bank index with Rwanda ranking the highest and South Sudan the lowest in the region (see Figure 14) These challenges are compounded as digital firms particularly those in e-commerce or sectors with licensing or certification requirements seek to expand regionally and must navigate these complex barriers to establish themselves in each country rather than expanding seamlessly across borders While large multinational firms have the resources to engage in this costly and time-consuming process and can tackle the registration process in countries simultaneously local start-ups often do not putting them at a disadvantage

An important part of the business environment is taxation policy92which is likely to affect entrepreneurship innovation profitability and choices concerning where to locate operations of digital firms within the region For93example reports suggest that taxes levied on tech start-ups in Kenya can be prohibitive94 As noted earlier high taxes on telecom equipment and services are suppressing investment in rural areas and consumer demand across the region Discrepancies in tax policy including tax rebates to attract companies can also cause friction between countries For example such incidents in the European Single Market have triggered reviews of related policy frameworks95 Any tax regime adopted in East Africa should thus balance the need to generate revenue and attract business with the need to avoid distortions to regional taxation policy East Africa can learn from the EU and should strive to adopt international best practice The Multilateral Competent Authority Agreement introduced by the Organisation for Economic Co-operation and Development (OECD) in 2016 advocates for automatic sharing of country-by-country information on the revenues and taxation of multinational corporations96

92 See httpreportsweforumorgglobal-information-technology-report-2016networked-readiness-index

93 See httpwwwdoingbusinessorgrankingsregion=sub-saharan-africa

94 Based on consultations feedback from Kenya

95 Companies raising concerns in Europe include not only Apple Google and Amazon but also Starbucks and Fiat httpswwwtheguardiancomworld2016aug30after-apple-the-other-tax-deals-in-the-european-commissions-sights

96 See httpwwwoecdorgnewsrooma-boost-to-transparency-in-international-tax-matters-31-countries-sign-tax-co-operation-agreementhtm

Figure 14 Ease of doing business index 2016ndash2017

Country Global ranking (out of 190) 2016

2017 Sub-Saharan Africa ranking (out of 48)

Rwanda 56 41 2

Kenya 92 80 3

Uganda 115 122 12

Tanzania 132 137 15

Burundi 157 164 34

South Sudan 186 187 46

Source World Bank June 201792


Complexities also arise where for example online services are hosted in one country but served in another or when infrastructure is deployed between countriesmdashcreating potential for duplication of taxation in multiple countries A recent example includes the dispute between the Kenyan Revenue Authority (KRA) and the Wananchi Group which offers satellite content over its tax status The KRA has argued that while the Wananchi Group is registered in Mauritius it had managed some if its services from Kenya However the Wananchi Group claimed its operations were independent and should be separately audited97

East Africa has also seen the rapid emergence of a vibrant innovation and start-up ecosystem Start-ups are key sources of innovation and due to their agility are better positioned to be ahead of the curve in technology adoption and adaptation They could be a key resource to revolutionize traditional brick and mortar industries across East Africa which are currently losing their competitiveness

Technology and innovation hubs incubators and accelerators are key in supporting this innovation ecosystem and are becoming increasingly common across East Africa Start-up accelerators support early-stage growth-driven companies through education mentorship andor financing Start-ups enter accelerators for a fixed period and as part of a cohort of companies Start-up incubators support the creation and initial growth of new and early-stage enterprises through access to resources such as capital physical space networking connections and mentorship Their support can last for a longer period than the support offered by accelerators The period can be at minimum six months however some incubatorsrsquo support lasts longer They offer a pathway to the first investment for start-ups Rapid technical skills training providers or technology bootcamps offer short-term applied intensive technology skills training paired with collaborative problem-solving and other soft skills development These entities provide a space where entrepreneurs can go online share ideas develop skills and meet potential investors and partners They often also help companies navigate the complex regulatory and legal environment at both the national and regional level In the past innovation hubs have successfully facilitated the creation of new apps and services that respond directly to local needs and consumer demand 98

97 See httpwwwtechweezcom20160816kra-zuku-3-4-billion

98 See httpwwwinfodevorginfodev-filesmobile_outcome_assessment_02-06-2014_last_version_1pdf

In 2016 of some 173 tech hubs and incubators located across the continent 35 could be found in East Africa99 However over a two-year period this figure has doubled across Africa and in the regionmdash30 hubs are now estimated to be located in Kenya alone and 16 hubs are located in Uganda which feature a number of large start-ups100 Uganda tech and innovation space HiveColab is currently supporting tech start-ups in the financial education and agriculture sectors including the agricultural information app mFarm101 KLab in Kigali is providing an open space for IT entrepreneurs and regularly hosts workshops bootcamps hackathons and networking sessions KLAb success stories include Academic Bridge a tech education start-up and AirClerk a cashless payment system 102

Currently many such entities operate on a national level however the most successful are starting to draw participants from across the subregion These networks are providing a supportive breeding ground for start-ups in the region The creation of an SDM in East Africa could accelerate the development of these regional networks and collaborations which could in turn speed up technology adoption and adaptation across East Africa and beyond mFarm was for example originally incubated by the mLab in Kenya and is now spreading to other countries in the regionmdashit is thus a pertinent example of the potential of regionally networked tech hubs to support technology diffusion Regional programs could also encourage more female entrepreneurship

99 See httpblogsworldbankorgic4dimportance-mapping-tech-hubs-africa-and-beyond figures from 2016

100 See httpswwwgsmacommobilefordevelopmentprogrammeecosystem-acceleratorafrica-a-look-at-the-442-active-tech-hubs-of-the-continent

101 See httpshivecolaborg httpsthenextwebcomafrica20110531why-nairobi-is-exploding-as-the-tech-hub-of-east-africa-interview-with-erik-hersman

102 See httpsklabrw

ldquo Technology and innovation hubs incubators and accelerators are key in supporting this innovation ecosystem and are becoming increasingly common across East Africardquo


Figure 15 Tech hubs and incubators by country

Source World Bank 2016 GSMA 2018103

Note 2018 data 1ndash4 in Burundi Rwanda and South Sudan 5ndash9 in Tanzania

However more can be done to support the regional acceleration of start-ups as well as established technology companies with high growth potential The expansion of these companies could for example be boosted through a coordinated regional program that includes acceleration support for scale-up of proven startup concepts which is often lacking in the region

By fostering the development of more regional incubation and acceleration programs East Africa could also become a more-attractive destination for venture capitalists and investments from multi-nationals offering a larger pipeline of promising start-ups A more integrated network would also help to internationalize the talent- coach- and mentor-base across the region which could strengthen start-up teams and collaboration leading to enhanced product and business development across the continent A larger

103 Figures from 2016 See httpblogsworldbankorgic4dimportance-mapping-tech-hubs-africa-and-beyond Figures from 2018 See httpswwwgsmacommobilefordevelopmentprogrammeecosystem-acceleratorafrica-a-look-at-the-442-active-tech-hubs-of-the-continent

market could also help drive improvements in the quality and targeted support offered to clients of such entities All these benefits have been recognized by AfriLabs which leverages a network of some 50 technology and innovation hubs across more than 20 countries in Africa to support new enterprises104

Some countries are consciously supporting business innovation through public programs The Uganda National Council for Science and Technology for example seeks to offer innovative leadership in the development and promote the application of science and technology and its integration in sustainable national development105 Greater regional coordination between innovation agencies could again reap the benefits offered by a larger network while promoting best practice across the region106

Access to finance and investment are critical factors affecting the growth of innovative start-ups in the region as well as the ability of established companies to expand into regional and international markets Many companies in Africa find it challenging to obtain debt and equity financing to support their growth due to both high collateral requirements and interest rates The former is impractical for many start-ups and SMEs The basic lending interest rate in Kenya was 148 percent in Burundi 17 percent in Kenya and 213 percent in Uganda 2016 whereas most developed economies typically offer similar loans at low single-digit rates107

The level of venture capital in the region is still low compared to the rest of the world though the situation is improving The Global Competitiveness Index from the World Economic Forum (WEF) rates venture-capital availability in East Africa In this index Uganda and Rwanda scored 24 and 34 out of 7 respectively compared to the United States which tops the ranking with a score of 52 out of 7108 Historically venture capital and private equity firms have tended to fund more established businesses over start-ups However new investors are emerging in the region Firms like Savannah Fund are providing seed capital to high-growth technology start-ups in East Africa which cannot yet attract venture-capital investment109 Several lsquoangel investorrsquo

104 See httpdisrupt-africacom201706afrilabs-welcomes-new-hubs-into-pan-african-network

105 See httpsuncstgougwho-we-are

106 See httpselibraryacbfpactorgacbfcollectacbfindexassocHASH01ade44e7241b749d69a1a6cdirACR201720Englishpdf

107 See httpsdataworldbankorgindicatorFRINRLEND

108 WEF Global Competitiveness Index 2018 Venture capital availability Burundi (26) Kenya (29) Rwanda (34) Tanzania (24) Uganda (24) and South Sudan (no data)

109 See httpsavannahvcabout

0

20

10

30

40

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

1

16

6

30

16

1

4 7

2018

2016


networks also exist in the region providing funding to early-stage start-ups Examples include Intellecap which launched an East African chapter of its angel network in 2015110 Another growing source of investment is crowdfunding which is coordinated by organizations such as Jumpstart Africa111 Moreover capital is being sourced through grants and competitions In 2017 Ecobank ran a lsquoFintech Challengersquo event awarding funding and expert mentoring to winning proposals112

Local capital markets are also growing offering investment in local currencies that limits currency-exchange risks Local investors will also often have a better understanding of the local market allowing them to make more informed investment decisions A technology project investment fund (TPIF) is being spearheaded by the Northern Corridor Technology Alliance (NCTA) which will source financing from local banks and equity pools

The scale offered by the SDM should make it easier for many businesses to attract debt or equity financing by demonstrating greater growth potential diverse market exposure and spreading investment risks This should also attract greater levels of foreign direct investment (FDI) However stronger SDM branding could also help promote the potential of East Africa to global investors

An additional factor constraining the development of the regional digital economy is the persistent digital skills deficit both in terms of the basic digital literacy that consumers require to use digital services and the availability of the more advanced ICT skills needed to support development of the tech industry Large parts of the population remain offline A lack of familiarity and training is often cited as a key barrier to adoption lack of digital skills was cited as the main reason for not going online by 45 percent of those surveyed in Tanzania and 37 percent of those surveyed in Kenya in a recent GSMA survey113 Gaining basic digital skills is an even greater challenge for those who also lack basic literacy which is estimated to be roughly 15ndash30 percent of the population in most East African countries and far higher in South Sudan (see Figure 16)114

110 See httpdisrupt-africacom201510intellecap-launches-angel-investment-network-in-east-africa

111 See httpswwwindiegogocomprojectsempower-africa-s-entrepreneurs-let-s-jumpstart-africa

112 See httpdisrupt-africacom201705finalists-announced-for-ecobank-fintech-challenge

113 See httpswwwgsmaintelligencecomresearchfile=8170bf058e42cdb8c186c6c75fb2b30eampdownload

114 Latest figures available from 2015 See httpsdataworldbankorgindicatorSEADTLITRZS

Figure 16 Adult literacy rate 2015


All countries in the region currently implement a range of domestic initiatives to promote basic digital literacy including programs to deliver ICT education in schools Yet teachers often lack the training to fully incorporate ICT in education Schools also lack internet connections and students lack access to digital devices In 2016 the Rwandan government approved the inclusion of ICT training in their national education policy with the aim of supporting the implementation of lsquosmart classroomsrsquo in all schools by 2019115 Regional collaboration could leverage examples of best practice in the region and make the development of shared educational resources more cost-effective

Community hubs can also become centers for learning where training to those above school age can be provided In some cases these centers can also provide the only affordable access to connectivity available adopting a free-usage or shared-access

115 See httpmineducgovrwfileadminuser_uploadpdf_filesICT_in_Education_Policy_approvedpdf

0

20

100

40

60

80

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

85

78 74

32

71

80


model One example is the Maarifa Centers established by the Arid Lands Information Network (ALIN) across Kenya Uganda and Tanzania which train local communities in basic digital skills that are directly relevant to themmdashsuch as how to use mobile phone applications to improve incomes from agricultural production116 Another example is UTouch which operates lsquodigital centersrsquo in remote villages in Uganda117

As the SDM develops so should the demand for basic digital skills training As new emerging online services and local content is developed the internet should become more attractive to non-users and more attuned to their needs However more advanced digital skills will be required by those developing said services and content A 2015 survey found that Kenyans prefer downloading foreign apps due to the poor quality of local apps and the lack of original or useful content118 Building a world-class ICT workforce with advanced digital skills is therefore necessary to boost local development of high-quality technology products Moreover many jobs associated with the fourth Industrial Revolution noted earlier will require highly specialized skills in science technology engineering and mathematics

A growing number of colleges and universities offer ICT-related courses Specialist technical institutions are also emerging such as Carnegie Mellon University in Kigali 119 or the African Advanced Level Telecommunications Institute (AFRALTI) which is active across Kenya Tanzania and Uganda120 The World Bank is also harnessing economies of scale by funding the creation of regional centers of excellence for technical training across Southern and Eastern Africa which includes centers in Kenya Rwanda Tanzania and Uganda

These initiatives will also benefit from ongoing efforts to harmonize the higher education and training systems in the region The EAC endorsed the establishment of a Common Higher Education Area (CHEA) in May 2017 which will promote common standards across the region and enable the mutual recognition of qualifications to facilitate labor mobility across the region However the Inter-University Council of East Africa charged with

116 See httpwww3weforumorgdocsWEF_Internet_for_All_Framework_Accelerating_Internet_Access_Adoption_report_2016pdf

117 See httpu-touchorg

118 See httpbuzzkenyacommobile-phone-users-kenya-prefer-foreign-apps-kenyan-made-apps

119 See httpwwwcmueduafrica

120 See httpwwwafraltiorg

implementing the initiative currently lacks funding Equally more resources will be required to support harmonization of teaching standards at the national level121

The free movement of students and skilled professionals in the IT field is another important input to the regional digital economy Alongside more cooperation on training greater harmonization of labor laws and training across the region could help both identify and fill skills gaps in the region Work-permit restriction has seen countries such as Tanzania experience loss of skilled employees and entrepreneurs to other countries in the region122 Differences in labor regulations across the regional market can also create arbitrage

Meanwhile companies in the region reportedly still struggle to find the technology talent they seek which suggests that both the quality and availability of existing training needs to improve In the interim this skills gap is being bridged through short-term immersive and applied training programs or so-called lsquoboot campsrsquo that often aim to improve participantsrsquo coding skills Some initiatives also specifically target women and girls Prominent examples include Nairobits that offers training in coding and web design to women123 Closer public-private collaboration in developing new training programs can also both tailor skills generated to industry needs and maximize the resources available to digital education

Ultimately the policy coordination and harmonization of laws and regulation needed to move toward an SDM will require bold leadership While many regional institutions are actively working in this space (detailed in Annex C) coordination and capacity building are needed to support the successful implementation of the initiative avoid duplication and maximize available resources The SDM initiative proposed should thus place a strong emphasis on working through regional institutions capitalizing on momentum created by existing integration efforts and on providing technical assistance

121 See httpiuceaorgeahea1declaration

122 See httppwcblogscomlegal201609new-requirements-for-work-permit-applicants-following-under-new-immigration-law-tanzaniahtml

123 See httpwwwnairobitscomabout


4 Conclusions and next steps toward implementation

Achieving an SDM would have tremendous benefits for East Africarsquos citizens governments and companiesmdashincreasing the regionrsquos competitiveness growth and job creation and enabling it to excel in the economy of the future However getting there will not be easy Realizing this vision will require a long-term strategic perspective bold leadership and at times a willingness to sacrifice elements of national sovereignty and short-term comparative advantage for the sake of a much larger but possibly more distant reward for all

This report presented a vision for an SDM and an assessment of the strengths weaknesses and opportunities that must be addressed and captured to achieve an SDM The annexes that follow present a detailed roadmap for achieving that vision (Annex A) along with a scorecard (Annex B) to measure progress toward

implementation It also outlines the key regional institutions and ongoing regional initiatives that will be leveraged to achieve the SDM objectives (Annex C) Finally the detailed economic analysis estimating the significant boost to GDP growth and job creation as a result of implementing the SDM is included as Annex D

By taking proactive action and working together rather than in competition the result can be mutually beneficial for all East African countries whether they are more or less advanced in their current digital development While countries that score higher in their digital scorecard can anticipate increased market access and options for services countries that present smaller markets and or are currently scoring lower will expect to see benefits from increased investment services and infrastructure Most importantly the SDM can help close the lsquodigital dividersquo among East Africarsquos people ensuring that everyone has access to and is benefitting from digital technologies rather than just a privileged few

Looking ahead the Digital Roadmap is expected to provide the basis of coordinated efforts by East African governments regional institutions donor partners NGOs and private sector to implement the recommendations The World Bank Group as a neutral broker of knowledge and expertise and as a multilateral financing institution is well positioned and prepared to contribute toward this effort leveraging its instruments for investment and technical advisory support as well as its convening power to bring together relevant stakeholders and draw attention to this important agenda

ldquo The Digital Roadmap is expected to provide the basis of coordinated efforts to implement the recommendationsrdquo


Annex A

SDM RoadmapThe SDM Roadmap details the recommended policy reforms and investments corresponding to each market layer of the strategic framework and the analysis of current market strengths weaknesses and opportunities in Section 3 Each action identified has been assigned an estimated implementation

completion time frame (a) short term (1ndash2 years) (b) medium term (2ndash3 years) and (c) long term (3ndash5 years) Each action has also been assigned a priority order (1st or 2nd order priorities) indicating the relative importanceimpact


Action Level Time Frame Priority Order

Objective Stimulating connectivity infrastructure development

Undertake PPP investments in regional fiber backbones to increase capacity redundancy and competition between networks including opportunities for co-deployment with regional linear infrastructure (roads pipelines power transmission)

Regional Mediumndashlong 1st

Coordinate national PPP investments to promote universal broadband access in each East African country (last mile connectivity)

National (regional coordination)

Mediumndashlong 1st

Coordinate a reduction in taxation fees and procedures for infrastructure deployment

bull Reduce or eliminate import duties for network equipment

bull Reduce fees and provide easier access to rights-of-way to lay cables and mount equipment along other public infrastructure (such as roadways electricity transmission lines and streetlight poles pipelines rail and so on) and

bull Lower cost or free licensing and spectrum allocation for infrastructure and services deployment in rural areas

National (regional co-ordination)

Shortndashmedium 1st

Develop and implement best practice guidance for the following

bull Infrastructure deployment (for example ducting specifications and use of multiple fiber pairs)

bull Coordination with other infrastructure projects to encourage parallel fiber deployment (employing a lsquodig once policyrsquo) and

bull Regional infrastructure sharing and wholesale access (for example related to duct access and dark fiber provision)

Regional

(with national implementation)

Short 2nd

Investigate the feasibility of a single regional 4G or 5G license (including coverage obligations for each country)

Regional Long 2nd




Develop policy to support trials of innovative technologies and business models for extending connectivity infrastructure and services

Regional


Medium 2nd

Objective Lowering regional transit costs for landlocked countries

Establish a low-cost open access regional backbone interconnection regime

bull Adopt regional open access policy including rights for any licensed operator in the region to purchase wholesale transit capacity and access submarine cables on non-discriminatory terms through any country in the region and

bull Establish regional glidepath for standardizing and lowering interconnection rates and transit pricing

Regional Medium 1st

Ensure that ISP licenses and IXP policies enable regional connectivity and traffic exchange at any IXP in the region

Regional


Medium 2nd

Objective Improving affordability and quality of connectivity services

Conduct a regional peer review and benchmarking of essential market competition regulations and policies in each country (including licensing interconnection number portability infrastructure sharing price regulation of dominant market players and so on) and implement or update policies where deficient

Regional


Short 1st

Coordinate a regional reduction or elimination of import and services taxes for consumer devices and connectivity services (by lowering consumer costs reducing tax arbitrage and leakage of devices and portage of data or voice bundles across borders from lowest taxed domiciles resulting from ONA implementation)



Extend ONA coverage to more countries and services

bull Extend ONA for voice to Tanzania and Burundi and

bull Extend ONA to cover data services in all countries

Regional


Short 1st


SINGLE DATA MARKET


Objective Removing restrictions to cross-border data flows storage and processing

Remove any undue restrictions to the free flow storage and processing of data across countries in East Africa and globally (remove data sovereignty requirements for non-essential data)


Short 1st

Review legislation relating to intermediary liability for third-party data to ensure that content created in one country and hosted or made available in another does not face undue restrictions

Regional


Medium 2nd

Objective Improving data privacy and cybersecurity

Develop and implement regionally harmonized data protection and privacy laws and regulations including explicit references to cross-border data flows definitions of personal and sensitive data and accounting for emerging services (for example cloud services data analytics)

Regional


Medium 1st

Establish a regional cybersecurity task force building on that proposed by the NCIP The task force should include all six countries and aim to

bull Drive collaboration on the detection and prevention of cybersecurity incidents

bull Implement a regional IT platform over which to share cybersecurity information and

bull Coordinate on developing and delivering national cybersecurity awareness raising and training programs for businesses government agencies and citizens

Regional Medium 2nd

Objective Increasing access to data for development of digital services and analytics

Digitize key government registries using regionally harmonized data standards to enable future cross-border data exchange (for example to facilitate one-stop border posts or Know Your Customer (KYC) requirements) and the creation of larger regional data sets essential for data analytics and development of regionally relevant data-based services


Mediumndashlong 1st

Develop and implement a regional open data initiative to make regionally standardized data sets available to public and private sectors to stimulate data-driven services analytics and innovation

Regional


MediumndashLong 2nd

Objective Promote development of regional data centers and cloud services

Encourage the development of high securityperformance (tier 3) regional data centers through outsourcing of government data storage and cloud services to private sector andor the development of shared public-private data center facilities encourage backupdisaster recovery through data centers in other East African countries

National Shortndashmedium 1st




Objective Facilitating seamless low cost cross-border digital payments

Establish full interoperability between mobile money networks (domestically and regionally) with a glidepath toward lower fees for cross-platform and cross-border transactions pursue initiatives to reduce currency exchange fees as interim solution (in the absence of a single currency union)

Regional



Support South Sudan in the licensing of mobile money services National Short 1st

Harmonize laws and regulations that affect the availability and ability to use lsquotraditionalrsquo electronic payment platforms such as Visa and PayPal


Medium 2nd

Objective Enabling cross-border identification and verification for digital transactions and services

Establish mutual recognition of national digital IDs and a regional platform for identification verification by governments and digital services providers to enable cross-border data exchange and to meet KYCuser authentication requirements for cross-border digital transactions


Short 2nd

Objective Harmonizing and modernizing legal and regulatory frameworks for digital transactions

Implement harmonized national e-transactions laws (including recognition of electronic signatures and harmonized consumer protection rules) in accordance with the EAC Electronic Transactions Act including for Burundi and South Sudan


Medium 1st

Objective Increase digitization and improve interoperability of public services and information systems (domestic and regional)

Develop an over-arching e-government strategy for the region including defining regional standards for ICT hardware and software procurement to ensure interoperability wherever possible

Regional Short 2nd

Digitize public services over time based on regional standards and work to integrate with regional services where appropriate

National Medium 1st

Objective Spur innovations in logistics to facilitate e-commerce deliveries (domestic and regional)124

Adopt and implement regionally harmonized address systems to enable parcel delivery including official adoption of alternative geospatial grid-based systems that can be rolled out instantaneously (for example what3words OkHi and similar approaches) to compliment traditional addressing systems which will take longer to implement and may not be well suited to rural areas

Regional

Short (alternatives)

long (traditional)

2nd

Adopt an opportunity-focused (as opposed to risk mitigation focused) approach to regulation of emerging logistics technologies (for example unmanned cargo drones) to foster regional innovation and support with investments in related infrastructure (such as drone ports)

Regional Long 2nd

124 In general domestic and cross-border logistics constraints are outside the scope of the SDM initiative and will be addressed through parallel efforts at the regional and national level such as through improvements in transport infrastructure one-stop border posts and efforts to remove nontariff barriers


ENABLING ENVIRONMENT


Objective Increase digital literacy and advanced skills

Invest in comprehensive digital literacy support programs

Ensure internet connectivity availability of tabletscomputers digital curriculum and training resources for teachers at primary and secondary schools

bull Support the development of community hubs where citizens can experience digital technology and receive basic digital skills training (web search mobile money social media and productivity software and applications such as word processing spreadsheets email and so on) and

bull Enable targeted digital skills programming and outreach for marginalized groups such as women the elderly the disabled and low-income households

National (with regional peer learning)

Mediumndashlong 1st

Implement rapid technology skills trainings such as coding boot camps with emphasis on inclusion of marginalized groups target industryworkforce relevant technical skills and general entrepreneurshipbusiness skills



Coordinate efforts to improve collaboration between regional tertiary education institutions on identifying digital skills requirements for the regional workforce developing relevant course materials and developing appropriate course materials and coordinating courses between institutions


Establish regional centers of excellence for highly specialized skills sets Regional Medium 2nd

Remove barriers (formal and informal) to the free movement of IT students professionals and entrepreneurs across the region and implement mutual recognition of IT qualifications in line with the East Africa Common Higher Education Area Commitments


Medium 2nd

Objective Enable and incentivize digital innovation entrepreneurship and content creation

Improve the regional support network for digital innovators and entrepreneurs

bull Facilitate lsquoupskillingrsquo and networking of national and regional tech hubs incubators and accelerators (using a hub and spoke model)

bull Cultivate a regional pipeline of start-ups (with a demonstrated proof of concept) ready to attract international venture capital or angel investors and


Regional and national

Medium 1st

Establish a regional body for intellectual property protection

bull Develop and implement harmonized regional standards for intellectual property protection

bull Ensure all countries are party to major international intellectual property agreements and

bull Consider establishment regional registration and enforcement of intellectual property rights

Regional


Long 2nd




Objective Attract FDI and access to capital for the regionrsquos tech industry

Support the development of a lsquoDigital East Africarsquo brand to attract investment

bull Develop a regional investment promotion strategy and marketing campaign and

bull Showcase a regional pipeline of start-ups to attract international venture capital and angel investors

Regional Short 2nd

Establish mechanisms to increase capital access for regional tech companies including through

bull Regional grant andor seed funding competitions

bull Regional equity investment funds and low-cost credit facilities and

bull Tax breaks for start-ups with gradual reversion to standard rates after reaching critical mass


Medium 2nd

Objective Leverage technology to overcome hard infrastructure constraints (power transport)125

Encourage access to energy for the unconnected through technology enabled solutions (as well as policy regulation and financial incentives)

bull Expand smart metering and the use of mini-grids and

bull Explore Pay-as-you-go (called lsquoPAYGOrsquo) home solar solutions

National

(with regional peer learning)


Improve cross-border logistics and the utilization of transport assets through digitization and the regional harmonization of data (including IT standards and greater interoperability)

bull Support the digitization of key registries and interoperability of critical information systems for cross-border movement of goods and people (including one-stop border posts citizens authentication customs immigration taxrevenue standards bureaus and so on) and

bull Make greater use of electronic cargo tracking

Regional


Mediumndashlong 2nd

Objective Strengthen Institutions Leadership and Decision Making

Provide financial and operational support to national governments in developing and enacting national policy and regulations that align with the Digital Roadmaprsquos recommendations


Short 1st

Work with regional institutions and national governments and regulatory bodies to improve regional ICT and digital economy statistics collection (including data reliability and the frequency of reporting) to enable improved decision and policy making

Regional Mediumndashlong 2nd

Increase the capacity and authority of EACO to serve as a regional regulatory advisor and monitor in collaboration with national regulators and industry


125 In general hard infrastructure constraints are outside the scope of the SDM initiative and will be addressed through parallel efforts at the regional and national level

45

Annex B

SDM Scorecard

The scorecard makes use of existing third-party data which is available in the public domain but there are limitations to this data relating to the completeness accuracy and the appropriateness of existing indicators

Data limitationsCompleteness Many of the selected indicators have gaps in terms of data availability for several countries Data points tend to be missing for South Sudan both due to its nascent statehood and weak national statistical capacity Capacity building related to digital economy statistics features in the Digital Roadmap to help improve data collection going forward

Accuracy Many indicators used are also difficult to accurately measure For example estimates for internet penetration reported by the ITU and national telecom regulators tend to vary greatly The scorecard therefore uses consistent sources across countries where possible (for example the ITU rather than data from individual regulators) to allow like-for-like comparison

Appropriateness Currently optimal indicators to track all elements of the SDM strategic framework are unavailable Most existing indicators are nationally focused and do not quantify cross-border barriers which are at the heart of the SDM Vision Moreover many are of a qualitative nature

As noted above coordinated efforts are required to address data gaps data standardization and measurement issues as well as the development of new more appropriate indicators Supporting disaggregated data for example in terms of gender will be important to ensure that the SDM benefits everyone The EAC is working to improve data collection and reporting in the region in cooperation with third parties such as the ITU however further work in this field is necessary Governments in the region should support this effort and improve the accuracy and timeliness of national indicators

Country-level indicatorsThe country-level indicators shown overleaf can be used to compare the current status of each country in East Africa as well as track the impact of the Digital Roadmap on the development of domestic markets over time A separate set of regional indicators are presented in the following paragraphs

Targets The targets selected reflect ambitious goals for connectivity which is viewed as foundational to the SDM initiative The targets selected for data online services and enabling environment are based on above-average performers in global benchmarkingrankings

ldquoThe SDM scorecard (lsquoscorecardrsquo) outlined in this section provides a comprehensive framework of quantifiable targets and indicators to measure progress toward achievement of the SDM Vision and an effective means of tracking implementation of the SDM Roadmap over time at both the national and regional levelrdquo


In the table below Red denotes low-end of regional range Orange denotes mid-range Amber denotes high-end of range nd = no data

Framework Category

Indicator Current Figures Target

Burundi Kenya Rwanda South Sudan

Tanzania Uganda

Connectivity (ICT development)

ICT Development Index - ranking (and score from 1ndash10) (ITU 2017)126

172

(148)

138

(291

153

(218)nd

165

(181)

152

(219) (7)

Connectivity (infrastructure)

2G network coverage - as of population

(GSMA 2017)127

(ITU 2016)

nd

53

95

94

100

100

nd

30

89

95

nd

9197



(ITU 2017)128

(GSMA 2017)129

40

40

78

80

921

939

20

20

85

305

64

4590



(GSMA 2017)130 107 25 643 nd 277 45 75


Presence of a nationwide fiber backbone Yes Yes Yes No Yes Yes Yes in all

Connectivity

(services)Mobile phone penetration - as based on subscriptions per 100 inhabitants (ITU 2017)

48 831 699 ~215 744 551 90


Secure internet servers - per 1 million people

(World Bank 2016)1311 11 6 0 2 2 15


Number of members of largest IXP

(Packet Clearing House 2017)1 30 14 0 36 8 40

Connectivity (services)

International internet bandwidth per Internet user (kbits) (ITU 2017)

~61 690 75 04 ~17 55 90


Internet usage rates - as of the population

(ITU 2017)

(Telecom regulators 2016)

~52

9

~26

90

~20

37

~67

nd

~13

40

~219

4970


Mobile-broadband penetration

- of active subscribers per 100 inhabitants (ITU 2017)

83 262 27 11 92 337 55


Mobile-broadband prices 500 MB - as GNI per capita

(ITU 2017) 1 GB - as GNI per capita

223

279

44

44

44

392

141

261

3

54

10

175

1

15

126 ICT Development Index 2017 See wwwituintnet4ITU-Didi2017

127 See httpswwwgsmaintelligencecommarkets1887datareport=5ad0b91030c4b

128 Measuring the Information Society Report Volume 2 ICT Country profiles June 2017 See httpswwwituintenITU-DStatisticsDocumentspublicationsmisr2017MISR2017_Volume2pdf


130 Ibid

131 Latest data available is from 2016 See httpsdataworldbankorgindicatorITNETSECRP6

47

Framework Category



Tanzania Uganda

Connectivity

(services)Fixed-broadband penetration - as based on subscribers per 100 inhabitants (ITU 2017)

0 03 02 0 34 03

Connectivity

(services)Household penetration of broadband - as based on subscribers (Telegeography 2017)

09 21 02 0 19 19 30

Data (data protection and privacy)

Data protection and privacy law (UNCTAD 2018)132 Draft Yes Yes Yes Draft Yes Yes

Data

(cybercrime)Cyber-crime laws (UNCTAD 2018)133 Draft Yes Yes Yes Draft Yes Yes

Data

(content regulation)

Intellectual property protection laws - as score from 1 to 7 in Global Competitiveness Index (WEF 2018)134

26 37 53 nd 32 33 55

Data

(open data)Open data - as score from 1 to 100 in Open Data Barometer (WWW 2016)135 nd 4042 1962 nd 2173 1159 50

Online services

(ID)Lack of ID ownership - of total population which is unregistered (World Bank 2018)136 27 18 20 53 47 49 5

Online services

(digital ID)Use of digitized ID system No Yes Yes Yes Yes Yes Yes

Online services (digital payments)

Mobile money usage - as of adults over the age 15 with mobile money accounts (Findex 2017)137

1 73 31 nd 39 51 90


Usage of digital payments - as of adults over the age 15 that have made or received digital payments over the past year (Findex 2017) 138

4 79 39 7 43 55 85

Online services (e-transaction laws)

E-transaction laws (UNCTAD 2018)139 Draft Yes Yes nd Yes Yes Yes

132 Online Consumer Protection Legislation Worldwide 2017 See httpunctadorgenPagesDTLSTI_and_ICTsICT4D-LegislationeCom-Consumer-Protection-Lawsaspx

133 Ibid

134 The Global Competitiveness Report 2017-2018 See httpwww3weforumorgdocsGCR2017-201805FullReportTheGlobalCompetitivenessReport2017ndash2018pdf

135 See httpsopendatabarometerorg4thedition

136 See httpsdatacatalogworldbankorgdatasetidentification-development-global-dataset

137 Latest data from 2017 See httpsglobalfindexworldbankorg - Burundi data from 2014

138 Ibid



Framework Category



Tanzania Uganda

Online services (consumer protection)

Consumer protection laws (UNCTAD 2018)140 Draft Yes Yes nd Draft Yes Yes

Online services (digital public services)

E-Government - ranking (and score) in global survey

(United Nations 2016)141

173

(023)

119

(042)

138

(034)

183

(018)

130

(035)

128

(036) 065

Online services (trade and customs)

Lead time to import - median case as number of days (World Bank 2016)142

15 days

3 days 3 days nd 4 days6

days2

days

Online services (logistics)

Logistics performance - based on global index (World Bank 2016 score from 1 to 5)143 25 33 30 nd 30 30 40

Enabling environment (digital skills)

Internet access in schools - as score from 1 to 7 in Global Competitiveness Index (WEF 2018)144 25 39 45 nd 31 31 55


Quality of math and science education - as score from 1 to 7 in Global Competitiveness Index (WEF 2018)145

37 43 44 nd 28 31 50


Local availability of specialized training services - as score from 1 to 7 in Global Competitiveness Index (WEF 2018)146

3 5 43 nd 4 43 55

Enabling environment (business amp innovation)

Number of procedure to set up a business - in Global Competitiveness Index (WEF 2018)147 3 7 5 nd 9 13 4


Venture capital availability - as score from 1 to 7 in Global Competitiveness Index (WEF 2018)148

26 29 34 nd 24 24 4

140 Ibid

141 UN E-Government Survey Latest ranking compiled in 2016 See httpspublicadministrationunorgegovkben-usReportsUN-E-Government-Survey-2016

142 Latest figures available date from 2016 httpsdataworldbankorgindicatorLPIMPDURSMDview=chart

143 Latest ranking compiled in 2016 httpslpiworldbankorginternationalglobal

144 The Global Competitiveness Report 2017ndash2018 See httpwww3weforumorgdocsGCR2017-201805FullReportTheGlobalCompetitivenessReport2017ndash2018pdf

145 Ibid

146 Ibid

147 Ibid


49

Framework Category



Tanzania Uganda


Access to credit - as score from 1 to 100 in Doing Business Survey (World Bank 2017)149 10 75 90 10 65 65 90


Total tax and contribution rate - as of profit in Doing Business Survey (World Bank 2017)150 415 374 332 314 441 338 30


Ease of doing business - ranking in the Doing Business Survey (World Bank 2017)151 164 80 41 187 137 122

Above 100


Innovation capacity - ranking (and score) in Global Innovation Index (African Capacity Building Foundation 2017)152

122

(2131)

80

(3095

99

(2736)nd

96

(2797)

102

(2697)

Enabling environment (hard infrastructure)

Quality of overall transportation infrastructure - as score from 1 to 7 in Global Competitiveness Index (WEF 2018)153

24 43 47 nd 36 33 5


Access to electricity - as of the population with access (World Bank 2016)154 76 56 294 89 328 267 75


Quality of electricity supply - as score from 1 to 7 in Global Competitiveness Index (WEF 2018)155

24 41 44 nd 31 34 55

Enabling environment (digital leadership and institutions)

Capacity - based on Africa Capacity Index (African Capacity Building Foundation 2016)156 534 552 682 nd 688 540 80

149 See httpwwwdoingbusinessorg~mediaWBGDoingBusinessDocumentsAnnual-ReportsEnglishDB2018-Full-Reportpdf

150 Ibid

151 Ibid

152 See httpswwwglobalinnovationindexorggii-2017-report


154 Latest figures available are from 2016 See httpsdataworldbankorgindicatorEGELCACCSZS


156 Latest figures available from 2016 httpswwwacbf-pactorgour-workhow-we-do-itknowledge-learningafrica-capacity-indicators


Regional indicatorsThe regional indicators listed below reflect cross-border barriers at each layer of the SDM that need to be eliminated Targets have been set against indicators illustrating the progress required to achieve the SDM Vision This scorecard is designed to evolve over

Framework Category Indicator Burundi Kenya Rwanda South Sudan

Tanzania Uganda Target


Cross-border interconnection of network infrastructure

No No No No No No Yes in all

Connectivity (services)International access to national backbone

Full Full Full na Partial157 Full Full for all countries



(ITU 2017)158 (GSMA 2017)159

40

40

78

80

921

939

20

20

85

305

64

45

Above 90 in all countries


Harmonized sector policy on taxation spectrum allocation licensing and rights-of-way

No No No No No No Yes based on international best practice


Price premium of 500 MB data bundle over cheapest country for prepaid data - as differential

nd 293 0 1355 442 344 No more than 50

Connectivity (services)Data and SMS roaming costs eliminated

No No No No No No Completing the lsquoONArsquo for East Africa

Data National data localization law

No No No No No No No localization requirements


Data protection and privacy law adoption (UNCTAD 2018)160

None Draft None nd Draft Draft Yes in all

157 Capacity can only be bought through ISPs licenced in Tanzania




time as new and better data becomes available Some indicators have thus been marked as lsquoto be confirmedrsquo in this iteration of the scorecard Regional indicators are not defined for the enabling environment


51



Data (cybercrime)Cyber-crime law adoption

(UNCTAD 2018)161

Draft Yes Yes Yes Draft YesYes in all

Data (cyberlaws)Regional harmonization of data policies and protocols

No No No No No No Yes based on international best practice AU law

Online services (Digital ID)

Adoption of interoperable Digital IDs (national)

In progress

Yes YesIn

progressIn progress Yes

Digital ID schemes in all countries


Regional recognition of digital IDs

No

Travel only (not

digital ID)

Travel

only (not digital ID)

No No

Travel

only

(not digital ID)

Online verification for online services across the region of digital ID

Online services (Digital payments)

Cross-border mobile money platform integration and interoperability



Regional cross-border transaction premiums eliminated

No No No No No NoPremiums eliminated


e-transaction law adoption

(UNCTAD 2018)162

Draft Yes Yes nd Yes Yes Yes in all


Regional harmonization of e-transaction laws



Consumer protection law adoption (UNCTAD 2018)163

Draft Law Law nd Draft Law Yes in all

161 Ibid

162 Ibid

163 Ibid





Regional harmonization of consumer protection laws

No No No No No NoYes based on international best practice


Number of regional nontariff barriers - in EAS common market scorecard164

5 23 10 nd 24 16No more than 5 nontariff barriers


Use of charges with equivalent effects on regional trade - number of charges noted in ECMA common market scorecard165

37 34 35 nd 28 9 None


Trading across borders - as score from 1 to 100 in Doing Business Survey (World Bank 2017)

4702 6763 7244 2619 2021 628 80


Number of regional centers of excellence for advanced digital skills development

Data to be collected

Enabling environment (business and innovation)

Number of regional entrepreneurship and innovation networks (incubation acceleration financing and investment promotion)


164 See wwweatradehuborgeast_african_common_market_scorecard_2016_tracking_eac_compliance_in_the_movement_of_capital_services_and_goods - page 14

165 Ibid - page 13


53

Annex C

A number of organizations are already working toward the goal of greater integration of East African economies The Digital Roadmap aims to build on the progress made by these organizations and to coordinate new activities with existing initiatives In this annex we summarize the current roles played by the following organizations

bull EACbull EACObull NCIPbull AUbull Other international initiatives

East African CommunityThe EAC is a regional intergovernmental organization whose mission is ldquoto widen and deepen economic political social and cultural integrationrdquo between the partner states After South Sudan joined the community in April 2016 there are now six EAC partner states which form the focus of the present SDM project The first major integration milestone achieved by the EAC was establishment of the Customs Union in 2005 whereby all partner states ldquoagreed to establish free trade (or zero duty imposed) on goods and services amongst themselves and agreed on a common external tariff (CET)rdquo166

This was followed by the establishment of the CMP in 2010 which is based on seven freedoms and rights

bull Free movement of goodsbull Free movement of personsbull Free movement of laborworkersbull Right of establishmentbull Right of residencebull Free movement of servicesbull Free movement of capital

166 See httpwwweacintintegration-pillarscustoms-union

Existing integration efforts and their role in the East Africa SDM

A 2016 paper by the International Growth Centre estimated that the EACrsquos CMP had led to a 213 percent increase in bilateral trade and that full implementation of a single market could lead to a similar magnitude of trade gains in the future167

The EAC has programs covering almost all economic sectors and four of these are directly relevant to the SDM Vision

bull Infrastructure - including communication links and hard infrastructure such as transport which is part of the enabling environment

bull Customs - aiming to establish a single customs territory to support regional trade which can help promote a single online market for goods

bull Education science and technology - harmonizing education systems and developing centers of excellence in the region which can help encourage skills training to promote increased infrastructure and online services in the region

bull Immigration and labor - supporting the free movement of people and labor in the region which can help promote innovation and technical support where needed

The EAC has put policies and guidelines in place for East Africa across a number of these areas However feedback from the consultation suggests that the interpretation and application of these policies can vary by country This is illustrated by the EACrsquos lsquoCommon Market Scorecard Reportrsquo a monitoring tool for the implementation of the CMP which shows that each country is implementing reforms at a different pace168

167 See httpwwwtheigcorgwp-contentuploads201606Mayer-and-Thoenig-2016-Working-paperpdf

168 See httpsd3n8a8pro7vhmxcloudfrontneteatradehubpages2893attachmentsoriginal1481012380East-Africa-Common-Market-Scorecard-2016pdf1481012380


A major milestone that will help support the SDM was the establishment of the EAC Legal Framework for Cyberlaws (Phase 1 of which was published in 2008 and Phase 2 in 2011) This framework is designed to be used for the reform of relevant national laws reflecting international best practice to facilitate e-commerce It is a broad framework with Phase 1 covering areas such as electronic transactions and digital signatures cybercrime data protection and privacy and consumer protection Phase 2 of the framework focuses on issues related to intellectual property competition taxation and information security169

The EAC will be an essential stakeholder in the implementation of the Digital Roadmap The organization already plays an important role in coordinating regional initiatives and in setting standards to promote harmonization of national laws and regulations However the EAC has not achieved all the goals set out at its inception Implementing the Digital Roadmap will require a much greater scale of regional intervention activities in the digital sector The SDM Digital Roadmap recommends that the capacity of the EAC is developed accordingly along with the appropriate resources to do so so that it can effectively drive progress toward the SDM Vision Of the six countries in the EAC four (Kenya Rwanda South Sudan and Uganda) are also involved in the Northern Corridor Integration Projects discussed below

East African Communications OrganizationEstablished in 2000 the EACO describes itself as ldquoa regional organization that brings together national ICT regulators operators services providers (in the telecommunication broadcasting and postal sub-sectors) ICT training institutions and other stakeholders in the communication sectorrdquo across the EAC countries170 EACOrsquos primary role is to support the integration of the regionrsquos ICT markets by driving the harmonization of ICT policy and regulatory frameworks Notably it has developed a draft regional regulatory framework alongside an implementation and monitoring framework to support adoption across member states171 EACO is also tasked with recommending best practice for a harmonized and converged licensing framework including guidelines for the management and allocation of spectrum

169 See httpunctadorgmeetingsenPresentationCII_EM5_P_RAchieng_enpdf

170 See httpwwweacoint

171 See httpwwweacointdocsWGsReportsEACO_WG01_Record_of_6th_Meeting_Arusha-November_2016pdf

The activities of EACO are organized under 11 working groups172

bull Policy and Regulatory Harmonization

bull Infrastructure Development Connectivity and Digital Inclusion

bull ICT Services and Applications

bull Postal Services Development

bull IP Networks Standards and Cyber Security

bull Broadcasting Services Development

bull Telecom Numbering and Internet Resources Planning Allocations and Governance

bull Communications Service Pricing and Industry Analysis

bull Quality of Service and Consumer Affairs

bull Environment and e-Waste Management

bull Spectrum Management

EACOrsquos ongoing initiatives are closely aligned with the goals of the SDM Vision particularly those related to a single connectivity market EACO could be a key stakeholder in the implementation of the Digital Roadmap for East Africa especially in areas relating to licensing and the use of spectrum along with other areas where policies would need to be implemented across the six countries EACO would need to work closely with the EAC as it sets policies and with the regulators in each of the six countries As with the EAC the greater workload would require increased capacity building and the appropriate resources

Northern Corridor Integration ProjectsThe NCIPs are designed to support the vision of ldquoA Northern Corridor that is fully integrated to improve the competitiveness of the region in the global marketrdquo173 The NCIPs are supported by the heads of state of Kenya Rwanda South Sudan and Uganda who attend summit meetings to discuss progress and agree actions These four principal partner states have recently been joined by the Democratic Republic of Congo and the last summit in Nairobi was also attended by Burundi and Ethiopia174 Tanzania is not currently involved in the NCIPs but it is engaged in a number of parallel and complementary initiatives through the EAC

There are 14 separate NCIPs each coordinated by one of the participating countries One of the NCIPs relates specifically to ICT but most of the projects will have some impact on the SDM Vision as summarized in Table C1172 See httpwwweacointindexphpworking-groups-committees

173 See httpswwwnciprojectsorgaboutabout-us


55

Table C1 The 14 NCIPs and their relevance to the SDM Vision

NCIP Coordi-nating Country

Relevance to SDM Vision

Air Space Management Rwanda bull Promoting lower-cost air travel and more routes that can help facilitate a single labor market

bull Facilitating drone delivery services for e-commerce in the long term

Mutual Peace Security and Defense Cooperation

Rwanda bull Supporting the security and stability required for a successful SDM

bull Covering cybersecurity as a central element of national security

bull Establishing a Joint Intelligence Center in Nairobi that requires data sharing between governments

Single Customs Territory

Rwanda bull Enabling the seamless flow of goods required for a single e-commerce market

Immigration Tourism Trade Labor and Services (ITTLS)

Rwanda bull Supporting the free movement of people for a single labor market (for example partner states have waived work permit fees for citizens of the Northern Corridor)

bull Facilitating the use of national identity cards as travel documents that can also help support a single online market

Land Kenya bull Harmonizing the land acquisition process that can help support a single infrastructure market (including rights-of-way and access to sites needed for data centers and other infrastructure)

Human Resource Capacity Building

Kenya bull Building capacity in numerous sectors including ICT

bull Supporting a skills audit for NCIPs the creation of centers of excellence for skills training and the removal of non-tuition fee barriers for studying in partner states

Financing Uganda bull Financing of the other NCIPs including ICT

Fast Tracking Political Federation

Uganda bull Providing an enabling governance structure for implementation of the SDM Vision

Commodities Exchange Kenya bull Expanding agricultural commodity exchanges that could be a key application for a single online market

Power Generation Transmission and Interconnectivity

Kenya bull Increasing the generation of power and facilitating power trade between states required to power ICT infrastructure and devices

Crude Oil Pipeline Development

Kenya bull Creating potential for coordination with telecom infrastructure rights-of-way

Oil Refinery Development

Uganda bull Facilitating expansion of supporting hard infrastructure

Standard Gauge Railway

Uganda bull Supporting the free movement of people and efficient transportation of goods to accelerate trade and services

bull Creating potential for coordination with telecom infrastructure deployment

ICT Infrastructure Uganda bull Coordinating ICT infrastructure which is a critical element of an SDM (discussed further in this section)

Refined Petroleum Products Pipeline Development

Kenya bull Creating potential for coordination with telecom infrastructure deployment


While all the NCIPs have an indirect impact on the SDM Vision the ICT infrastructure NCIP is directly applicable to the SDM Vision It identifies nine priority areas that should be addressed These are relevant to many areas of the SDM framework beyond simply ICT infrastructure as shown in Table C2

To implement the SDM Vision across all six countries the Digital Roadmap recommends considering whether to expand the Northern Corridor ICT program priority initiatives to Tanzania and Burundi potentially in coordination with the EAC It is noted however that the heads of state of the Northern Corridor countries have not met for over a year and thus an early step in the implementation process would be to determine the future of the NCIPs and how the NCIP and EAC programs can be rationalized to avoid duplication

Broader initiatives in AfricaBeyond East Africa there are several organizations whose goal is to increase integration and cooperation among countries on the African continent

The African Union is a political union of all 55 nations in Africa which aims to promote integration of the continent while tackling social economic and political problems The AUrsquos vision is to work toward ldquoan integrated prosperous and peaceful Africa driven by its own citizens and representing a dynamic force in global arenardquo176 The AU has established various standards and workstreams relevant to the SDM For example the AU members adopted a Convention on Cyber Security and Personal Data Protection in 2014 To facilitate implementation of the Convention lsquoPrivacy and Personal Data Protection Guidelinesrsquo were recently published detailing the roles and responsibilities of key stakeholders involved in data protection177 Notably the AU is spearheading the African Continental Free Trade Area (AfCFTA) which was signed into effect by 44 countries in March 2018 at an AU summit in Kigali Once it is ratified the AfCFTA will create a single continental market for goods and services as well as a customs union facilitating free movement of capital and business The initiative aims to boost intraregional trade which currently accounts for no more than 15 percent of total trade in Africa178 According to the United Nations Economic Commission on Africaldquo(UNECA) intra-African trade is likely to increase by 523 percent under the AfCFTA due to the elimination of import duties This figure is set to double upon further removal nontariff barriers179 A recent report advocating for the formation of an

176 See httpsauintenau-nutshell

177 African Union and Internet Society May 2018 Personal Data Protection Guidelines for Africa A joint initiative of the Internet Society and the Commission of the African Union

178 See httpsauintsitesdefaultfilespages32151-file-plenary_2__brief_on_intra_african_trade_and_investmentspdf

179 See httpswwwunecaorgstoriessigning-afcfta-giant-stride-forward-development-africa-ecaE28099s-vera-songwe

Table C2 Priority areas for the ICT infrastructure NCIP

SDM Vision Frame-work

Priority Areas for the ICT Infrastructure NCIP

Single connectivity market

bull ICT policy infrastructure implementation and broadband connectivity

bull Roaming charges and termination rates

bull Harmonization of sim-card registration regimes

Single data market

bull Cybersecurity

Single online market

bull E-services

bull Digital migration

Enabling environment

bull Mainstreaming of ICT in the integration projects

bull ICT skills and human capital development

bull Support for development of policy and regulatory framework to the Republic of South Sudan

Progress has been made against all of the NCIPs and the outcomes are published online following each NCIP summit175 The most significant achievement of the ICT project relates to the elimination of roaming charges under the East Africa ONA roaming program

While the NCIPs are led by the public sector the The Internet amp Television Association (NCTA) aims to coordinate the regional response of the ICT private sector The NCTA consists of business members that contribute to the NCIP project delivery within the Northern Corridor Governments in the Northern Corridor have agreed in principle on a regional PPP framework to facilitate coordination of effort with the private sector This is an encouraging development which could be expanded to cover the broader East Africa SDM

175 See httpnciprojectsorgpublications

57

African SDM under the AfCFTA suggests that many of the economic benefits emerging from the creation of an SDM will be realized by leveraging technology-based solutions grounded in Cloud Computing Services (CCS)180

The African Council of Regulators (ARC) was established in 2017 and now meets on a quarterly basis The ARC seeks to implement the lsquoOne Africa Networkrsquomdasha roaming agreement that builds on the successful East Africa ONA181 Moreover the ARC is working on issues such as access to ICT for schools and has established working groups in the following areas which are relevant to the SDM

bull Telecommunications

bull Connectivity infrastructure development

bull Numbering and internet resources planning and allocation

bull Cybersecurity and online privacy

bull ICT services and applications

bull Emerging technologies

bull Digital inclusion

The SMART Africa Alliance is another continent-wide organization that is addressing ICT issues It is tasked with implementing the SMART Africa Manifesto that was endorsed in 2014 by all African heads of state and the AU The Alliance is a multi-stakeholder partnership which includes member states multilateral organizations NGOs and the private sector It focuses on goals such as increasing access to digital technology and on key enablers including innovation and capacity building that are consistent with the SDM Vision Today the Alliance includes a wide range of countries across Africa including four of the six countries in East Africa (not including Burundi and Tanzania) SMART Africa can be an important partner for improving various aspects of the SDM Although its remit is broader than East Africa it can play a key role in promoting and coordinating the SDM agenda at the continental level For example it recently hosted the Transform Africa Summit in May 2018 which addressed the conference theme lsquoAccelerating Africarsquos Single Digital Marketrsquo182

There are currently also efforts to spur greater integration between different regional blocs in Africa One example is the


181 See httpssmartafricaorgeventspast-meetings-and-eventsarticle3rd-council-of-african-regulators-conakry-guinee-14-15-decembre-2017

182 See httpssmartafricaorgeventspast-meetings-and-eventsTransform-Africa-Summit-2018-09-10-May-2018-Kigali-Rwanda

planned Tripartite Free Trade Area between the EAC Southern Africa Development Community (SADC) and the COMESA This aims to create the largest single free trade area in Africa with a market of over 600 million people although there have been delays in the agreement being ratified by all countries183

These broader African initiatives share many of the objectives of the SDM and can support its development The East Africa SDM should seek to align with the standards and regulations adopted by these pan-African bodies In fact East Africa is in a position to build on existing regional ties to implement changes at a faster pace than other regional blocs in Africa However the SDM proposes to target much deeper digital integration than is likely to be feasible for the other regions in Africa or Africa as a whole As a result East Africa may also help lead the way for some of these broader African initiatives

Other international initiativesIn addition to the national governments and the regional bodies discussed earlier a range of international organizations are working on initiatives that support the SDM Vision As the Digital Roadmap is implemented it will be important to work closely with these organizations to coordinate initiatives and avoid unnecessary duplication of effort For example establishment of an SDM is a pillar of the WEFrsquos Internet for All initiative in East Africa Consultation meetings took place in Rwanda and Uganda under the auspices of said WEF initiative and it is hoped that WEF will continue to play a key role in the implementation of the SDM Digital Roadmap

Other sector-specific bodies are also likely to play a role in supporting the implementation of various aspects of the SDM For example the Universal Postal Union (UPU) has existing programs in areas such as logistics payments digital ID and consumer protection Cooperation with the ITU on regulation standards and data collection is also merited The Digital Roadmap also takes existing World Bank programs into account many of which have objectives that overlap with the SDM Vision Several such programs exist including ID4D and the East Africa Regional Transport Trade and Development Facilitation Project184

183 See httpallafricacomstories201707190091html

184 See httpwwwworldbankorgennewspress-release20150611world-bank-group-approves-500-million-for-eastern-africa-development-corridor


Background and acknowledgements This analysis has been carried out to estimate the economic impact of implementing an SDM in East Africa This includes assessing the impact of digital market integration on GDP growth and job creation at the macro level as well as the distribution of the expected benefits across income levels particularly for those at the bottom of the pyramid

It has been carried out with joint effort between teams at the World Bank Analysys Mason Limited and individual economists (Neil Gandal Edgardo Sepulveda and Ivan Gonzalez Berenguer Pena)

While this assessment focuses on the East Africa region specifically the methodology developed can be applied to assess the impact of moving toward an SDM in any regioncountry grouping

Summary of findingsThe analysis that follows shows that the benefits of integration toward an SDM in East Africa are significant particularly in the countries lagging in broadband availability and adoption In summary the paper shows the following

bull In the Base Scenario the average increase in GDP across the region is 057 percent five years post-integration or US$093 billion while the average increase in employment is 22 percent or approximately 16 million new jobs In the High Scenario GDP increase is in the range of 16 percent or US$26 billion and employment increase rises to 62 percent or approximately 45 million new jobs In both scenarios these benefits are based on the increase in mobile broadband adoption resulting from integration

bull Furthermore the analysis shows that existing broadband users will also benefit from regional integration as they will pay less for broadband services and will be able to access a wider variety and higher value of digital content and services as well as to connect with a wider network of users across the region Such savings will result in significant consumer benefit but will not be captured in GDP growth statistics

bull Finally as digital services such as mobile money become interoperable and competitive across the region the benefits will extend to the bottom of the pyramid as more and more citizens gain access to such services for spending earning and producing their own goods and services

These estimates even in the High Scenario are conservative as a more integrated and competitive regional market will further increase innovation technology adoption and investment across all sectors over the medium to long term fueling a reinforcing cycle of productivity gains growth and job creation for many years to come

Drivers of economic impact under an SDMAt its heart the SDM initiative is about creating the economies of scale and network effects necessary for the East Africa region to be a competitive player in the global digital economy The economies of scale and network effects generated through the SDM will have a ripple effect across the digital economy boosting broadband penetration and ultimately translating into GDP growth and job creation

For the purposes of this paper the latter (that is job creation) is estimated using the long-studied links between increased broadband penetration and job growth For GDP growth an extensive exercise that looks at direct and indirect benefits from changes in broadband penetration as a result of the SDM has been conducted

To assess GDP growth the general framework shown in Figure D1 which considers four different impact pathways was used

The four impact pathways are the following

1 The first effect results from the construction of broadband networks In a way similar to any infrastructure project the deployment of broadband networks creates jobs and has a multiplier effect throughout the wider economy

2 The second effect results from the lsquospilloverrsquo externalities which have an impact on both enterprises and consumers

Annex D

Economic Impact Assessment

59

bull The adoption of broadband within firms leads to a multifactor productivity gain which in turn contributes to growth of GDP

bull Residential adoption drives an increase in household real income

3 Beyond these direct benefits which contribute to GDP growth residential users receive a benefit in terms of consumer surplus

This last parameter while not being captured in the GDP statistics can be significant as it may represent benefits in terms of enhanced access to information entertainment and public services Therefore following the general GDP framework from the ITU this paper will examine the economic impact of an SDM in East Africa assessing spillover externalities (changes in GDP and jobs) and consumer surplus

The paper also considers the added value of an SDM in terms of the quality and variety of digital services available at a given broadband penetration level in an integrated regional market compared with an isolated country market

Figure D1 GDP impact general framework

Source ITU

Methodology and literature reviewAn extensive literature review was conducted to determine the best methodology to measure GDP growth jobs creation and consumer surplus See Figure D2 for papers consulted to determine the economic impact of broadband

Writ large the research and evidence-base reviewed falls into five categories

bull Contribution to economic growth (ldquopositive externalitiesrdquo)

bull Contribution to productivity gains

bull Contribution to employment and output of broadband deployment (ldquocountercyclical effectrdquo)

bull Creation of consumer surplus

bull Improvement of firm efficiencies

These five areas use a number of techniques and methodologies the most common of which are the following

bull Input-Output Analysis

bull Econometric Analysis

bull Consumer Surplus

Figure D2 Literature reviewTitle Author (Year)

The Impact of Broadband on Growth and Productivity

MICUS (2008)

The economic impact of broadband on growth A simultaneous approach

Koutroumpis (2009)

Broadband infrastructure and Economic Growth

Czernich (2011)

The Economic Impact of a European Digital Simple Market

Copenhagen Economics (2010)

The Economic Impact of broadband in Panama

ITU (2012)

Measuring (in a time of crisis) the impact of broadband connections on economic growth an OECD panel analysis

Angelo Castaldo Alessandro Fiorini and Bernardo Maggi (2016)

These methodologies have been used to answer two key questions

1 Does the economic impact of broadband increase with penetration and can we pinpoint a saturation threshold when decreasing returns to penetration exist

BROADBANDDEPLOYMENT

DIRECTBENEFITS

RESIDENTIALPENETRATION

ENTERPRISEPENETRATION

TOTALFACTOR

PRODUCTIVITY

HOUSEHOLDINCOME

CONSUMERSURPLUS

CONTRIBUTIONTO GDP

GROWTH

INVESTMENT ININFRASTRUCTURE

DEPLOYMENT


2 What explains the lagged effect of broadband in the economy

Some authors have pointed out a potential lsquosaturationrsquo effect meaning that beyond a certain adoption level (not specified as of yet) the effect of broadband on the economy tends to diminish These are key elements to consider when assessing broadbandrsquos impact in developing countries which typically have lower penetration and supply

Input-output analysis

Definition This approach focuses on determining how much value added and employment is generated through the rollout of high-speed broadband services The idea behind this model is that complex relationships develop between industries because each sector sources goods and services from other sectors Consequently investments in one sector indirectly trigger demand in other sectors as well These networked relationships mean that the effect of investment of a broadband rollout program is greater than the direct effects would suggest

Methodology The estimation of countercyclical effects comprises two stepsmdashthe estimation of investment required to fulfill the targets of the broadband plan and the calculation of resulting economic effects through input-output analysis

Limitations Inputoutput tables are not easily available in many countries including the ones being assessed for this report

Econometric analysis

Definition The available literature reflects a number of econometric studies to measure broadbandrsquos impact on GDP growth and employment by specifying regression models where GDP growth employment and other output metrics are a function of broadband deployment and penetration However due to the limited data availability most studies tend to focus on developed countries

Methodology There are three types of model-estimation procedures used to assess the economic impact of broadband These include (a) cross-sectional regression (b) panel data and (c) simultaneous equations

bull The cross-sectional regression relies on one observation per unit (country county region and so on) When studying change in variables at least two points in time are needed It includes

independent variables such as broadband penetration level of tertiary education fixed capital investment and the dependent variables (such as GDP or employment growth) This methodology is the most commonly used because it is rare that more than two years of data across each variable is available Given the need to determine the direction of causality it is common to lag the variables by collecting data for independent variables in year 1 and regressing them against dependent variables in year 2 or after

bull Panel data and simultaneous equations are two techniques that further help econometric analyses determine causation rather than correlation They are among the most successful techniques that have been employed in the papers that analyze broadband economic effects Panel data is a time series for multiple geographic areas (that is it is both a time series and a cross-sectional data set) This allows researchers to account for time fixed effects and geographical fixed effects

bull Simultaneous equations are used to deal with endogeneity or a cycle where factors cause the indicators to change and vice versa This problem is particularly pronounced in the study of broadbandrsquos effect on GDP GDP per capita and income When there is sufficient data this approach is optimal

Limitations The key disadvantage of the econometric analysis particularly for the simultaneous equations approach is the lack of data available especially in developing countries

Consumer surplus of broadband

Definition The theoretical framework for the measurement of broadband consumer surplus is based on the notion that a new good (in this case broadband) provides benefits that are additional to the old (dial-up access) It is not as common in the literature as econometric studies

Methodology The objective of this methodology is to calculate a metric for consumer surplus and net gain in producer revenue expressed in a single currency for comparability

Limitations The model measuring consumer surplus originated from broadband services presumes a stable demand as core factors shaping demand do not change substantially In that sense consumer surplus results are quite valid for the short run On the other hand the analysis can yield conservative estimates because they might exclude gains to early adopters shifts in demand linked to GDP growth falling prices of personal computers greater capability of online system and changing user willingness to pay Furthermore the methodology excludes indirect benefits

61

Therefore the preferred approach to measure SDM impact in East Africa is one that uses panel data econometric analysis using broadband penetration rates as proxy to evaluate spillover effects (GDP growth and jobs creation) and a separate assessment of the resulting implications in terms of consumer surplus Broadband saturation effect and the lagged effect of broadband in any economy are key variables to take into account in the model

Figure D3 Network externalities and critical mass effect

Network Externalities and Critical Mass Effect

A critical element of the evolving theoretical framework of network externalities of broadband is the impact that infrastructure penetration levels may have on output Is there a linear relationship between broadband adoption and economic growth Or are we in the presence of a more complex causality effect

The lsquocritical massrsquo findings of research of the impact of telecommunications on the economy indicate that the impact of broadband on economic growth may only become significant once the adoption of the platform achieves high penetration levels Theoretically it appears that there is a nonlinear (or S-Shaped) relationship between broadband penetration and output

At low levels of broadband penetration the impact of broadband on the economy is minimal due to the lsquocritical massrsquo concept The impact on the economic output is maximized once the infrastructure reaches a critical mass point generally associated with levels of penetration of developed countries Then once it reaches the saturation point impact on economic output diminishes and tends to zero

The implication of this finding for developing countries is significant Research points to the fact that to achieve an important level of economic impact broadband needs to reach high levels of penetration In this regard it is worth highlighting Koutroumpis (2009)185 who finds that the contribution of broadband to economic growth increases with penetration (based on a study of the OECD countries) According to this research in countries with low broadband penetration (under 20 percent) an increase of 10 percent in broadband adoption contributes to 008 percent to GDP growth while in countries with medium penetration (between 20 percent and 30 percent) the effect is a 014 percent contribution and in countries with penetration higher than 30 percent the impact of 10 percent adoption reaches 023 percent

185 See table D2 for full reference

Macroeconomic impactIn this section we look at the impact of an SDM on GDP and jobs based on an increase in mobile broadband adoption as well as the benefits that will accrue to existing mobile broadband subscribers through lower prices and increased network effects (that is consumer surplus)

Methodology

The macroeconomic analysis can be broken down into four parts

bull Part A Using panel data econometric analysis we determine the impact of price network effects and broadband availability on broadband adoption in African countries in general This provides the parameters used to measure the increase in broadband adoption resulting from the SDM

bull Part B We then estimate the impact of integration in East Africa in two different scenarios Base and High We estimate (a) how integration impacts price reductions for broadband (b) the level of network effects across the six countries resulting from integration and creation of a larger market and (c) the increase in broadband availability for the two different scenarios Combining these estimates with the parameters from Part A we can determine the increase in broadband adoption in each of the six countries

bull Part C Using the increase in broadband adoption figures from Part B we estimate impacts on GDP and jobs for both the Base and High Scenarios For jobs previous ITU findings on broadbandrsquos impact on jobs is utilized In the case of GDP an additional panel data econometric analysis was conducted to determine broadbandrsquos impact on GDP growth based on penetration level

bull Part D Finally in addition to an increase in the number of users the SDM will deliver benefits to existing users through access to a wider network of users a broader range of digital content and services as well as through lower broadband prices We estimate these consumer surplus benefits for the Base and High Scenarios as well

bull To summarize the methodology as an example we look at how we would estimate the impact of changes in one of the variablesmdasha price fall in each country resulting from the SDMmdashas set out in Table D1


Table D1 Summary of methodology

Part Objective

Part ADetermine the percentage increase in broadband demand a caused by a 1 percent decrease in the price of broadband We will do the same for network effects and broadband availability

Part BEstimate the percent b that broadband prices will fall based on the SDM to get the total increase in broadband adoption Multiplying by a gives the increase in broadband adoption expected in each country c Calculations for the Base and High Scenarios

Part CUse ITU multiplier for jobs and the GDP multiplier calculated for the purpose of this paper to determine GDP and job increases in each country c Calculations for the Base and High Scenarios

Part D

Show for each country how the decrease in price b will save money for existing users and how the increased network effects from the SDM will make the Internet more useful for these users raising their consumer surplus Calculations for the Base and High Scenarios

Part A Elasticities of demand for broadband

In this section we estimate the impact of integration on broadband penetration using econometric analysis based on existing data from the ITU GSMA Intelligence and the World Bank To ensure relevance the analysis uses data from all countries across Africa and uses a time series from 2012 to 2016 to increase the significance of the results The estimates are used

to assess the elasticities of demand for broadband access (that is how sensitive demand is to price network effects and availability of broadband) which will then be utilized to determine increase in broadband penetration due to the SDM The equation used to determine these elasticities is featured in Table D2

63

Table D2 Equation used to determine elasticity of demand

Variable Definition

Dependent variable

Penetration rate for mobile broadbandThis variable measures the number of people with mobile broadband subscriptions This is for unique subscribers rather than the number of total connections to control for some users having multiple subscriptionsSIMs

Independent variables

Price of mobile prepaid serviceThe variable measures the cost of 500 MB of mobile broadband data with a prepaid plan using a mobile handset in US dollars

International bandwidth in Mbits

This measures the amount of international capacity that is used for Internet services

As more users go online and use more services they are generating more traffic which uses more bandwidth As such bandwidth is a good metric to measure total Internet usage in a country While there is no metric available for total bandwidth used within a country international bandwidth used is a good estimate as currently up to 90 of usage is international in countries in East Africa

Furthermore the amount of bandwidth used is a good proxy for network effects which arise when the benefits of a service for each user increases exponentially with the number of users of that service By and large traffic is generated by communications between users including emails and video conferences and by interactions with online content and services such as YouTube and websites The former traffic represents direct network effectsmdashthe more users there are to communicate with the more traffic and the greater the benefit of going online The latter traffic represents indirect network effectsmdashthe more users there are the greater the amount of data content and services are available and the greater the benefit of going online

As a result the amount of online usage represented by international bandwidth used is a good proxy of network effects A market with higher online usage and corresponding traffic has more users to communicate with and more content and services being used making it more attractive for new users

Availability of broadband

This is measured by the percentage of the population that can receive at least a 3G mobile signal This is a necessary but not sufficient precursor of Internet adoption in a country As economies of scale for deployment of connectivity infrastructure and services grow across the SDM and demand rises from the previously discussed effects adoption will grow as network coverage increases

Household consumption per capita in USD

This measures the spending level in each country which is an important enabler of adoption of broadband all other factors being equal While the exercise in the present paper shows the benefits of the SDM on GDP and jobs which will ultimately affect income consumption levels we are holding the spending level constant for this analysis to focus on the direct effects of the SDM through the previous three metrics


Table D3 summarizes the 2016 figures for these variables in each of the six countries This is the starting point for our consideration of the economic impact of integration

Table D3 Pre-integration data for East Af-rican countries

Variable

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi

Unique mobile broadband subscribers (as a of the population)

5171 2913 2384 2384 854 1095

Mobile broadband price (US$)

246 230 254 584 931 483

International internet bandwidth (Mbits)

860570 12190 19024 49984 352 3510

Mobile broadband availability ( of population covered by signal)

82 85 92 45 20 40

Source GSMA ITU and World Bank 2016186

186 Unique mobile user penetration is from the GSMA The data series used has subsequently been revised by GSMA after this study was completed mobile broadband price is from the ITU International internet bandwidth is from the ITU household consumption per capita (not shown) is from the World Bank and mobile broadband availability is from the GSMA except for Tanzania which is taken from the ITU as one operator does not report to the GSMA

We summarize the results from the econometric regressions in Table D4 with further explanation of the methodology in Annex D1 Because the variables are logged the results can be interpreted as follows for international bandwidth the results indicate that a 100 percent increase in international bandwidth in any country is associated with a 14 percent increase in mobile penetration in that country Likewise for the other independent variables each of which impacts mobile penetration based on the parameter in Table D4

Table D4 Parameters from regression results

Log of Mobile Penetration

Results187

Log of price minus0068

Log of Intl_Bandwidth

015

Log of consumption per capita

220

Log of availability 046

Part B Impact of the SDM on prices network effects and broadband availability

In this section we make assumptions about how the SDM will affect prices network effects and availability of broadband in each of the six countries for two different scenarios Base and High We then use the assumptions about each of the three variables to see the impact of integration on mobile penetration across the countries the key variable to determine the impact on GDP and jobs in Part C

187 All coefficients are statistically significant - see Annex D1 for details

65

bull Price Based on the removal of barriers across the six countries including the elimination of mobile data roaming rates and duties on handsets we estimate that the prices in the Base Scenario will equalize across all six countries decreasing to US$2 This is a slightly lower rate than the current lowest market rate in the region (US$230) which is offered in Tanzania For the High Scenario we estimate that prices will decrease to US$15 across the six countries In the long run both scenarios may be conservative as economies of scale and competition are likely to further reduce rates even in those three countries where prices are currently low However in the short run the benefits in South Sudan Burundi and Uganda where prices are currently much higher are most significant

bull International bandwidth In an integrated market internet users in each country will benefit to a degree from the usage in neighboring countries due to the elimination of barriers allowing the free flow of data and services across borders To again be conservative in the Base Scenario we assume that network effects rise in each country by just 25 percent of the total international bandwidth in the other five countries For the High Scenario we assume that network effects will rise in each country by 50 percent Given the significant amount of bandwidth used in Kenya before integration188 the other five countries receive a much more significant benefit 189

bull Availability We assume that the SDM will lower the costs of deployment while also increasing demand thus resulting in greater mobile broadband availability than would otherwise be the case We assume that the increase would be 25 percent for the Base Scenario and as much as 50 percent for the High Scenario Please note that the availability was capped at 100 percent for Kenya Rwanda and Tanzania which were already close to full population coverage

Table D5 summarizes the assumed values of the variables after integration for the Base and High Scenarios

188 Note that we tested the extent to which Kenya along with South Africa act as outliers due both to the high usage in their countries and that the countries act as hubs for international bandwidth coming in from undersea cables and going out to neighboring countries As noted in the Annex removing Kenya and South Africa from the regressions (which include all African countries) or subtracting potential hubbed traffic does not affect the results statistically and thus the results are robust to these outliers

189 We also assume that the network effects cannot more than triple the pre-integration international Internet bandwidth as particularly in the countries with the very low starting point the total network effects were increasing by magnitudes based on the large starting point of Kenya We assume that there is a limit to the benefits that could be absorbed from such a low starting point

Table D5 Post-integration variables for East Africa Base and High ScenariosBase Scenario

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


2 2 2 2 2 2

International Internet bandwidth (Mbits)

881835 36570 57072 149952 1056 10530

Mobile broadband availability ()

100 100 100 56 25 50

High Scenario

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


15 15 15 15 15 15


903100 60950 95120 249920 1760 17550


100 100 100 90 68 69


Applying the regression parameters in Table D4 to the new post-integration levels of price international bandwidth and availability gives the following mobile penetration levels in each country for each of the scenarios being studied This is the impact after the integration has been implemented and the resulting post-integration broadband prices availability and networks

Table D6 Post-integration mobile broadband penetration for East Africa Base and High Scenarios

Base Scenario Kenya Tanzania Rwanda UgandaSouth Sudan

Burundi

Mobile broadband penetration before integration ()

52 29 24 24 9 11

Mobile broadband penetration after integration ()

58 37 30 34 12 15

Change in percentage points () 6 8 6 10 3 4

High Scenario Kenya Tanzania Rwanda UgandaSouth Sudan

Burundi


52 29 24 24 9 11


67 55 52 46 22 19


As described in the tables in this annex all countries benefit from integration with their neighbors as a result of lower prices greater network effects and broadband availability with increases in mobile penetration due to SDM ranging from 3ndash10 percentage

are achieved From there we expect that growth in broadband penetration would continue to accelerate as the changes begin to reverberate throughout the economy government and users (based on the increased availability and value of online services content and commerce plus the falling cost and increasing quality of broadband)

points in the Base Scenario to 8ndash28 percentage points in the High Scenario Countries that started with the midrange mobile penetration levels (that is Tanzania Rwanda and Uganda) see the greatest increase in penetration in both scenarios

67

Part C Impact on GDP and jobs

As noted in the literature review increases in broadband adoption have a macroeconomic impact on GDP and jobs To determine the SDMrsquos impact on jobs we use ITU studies which show that for every 1 percent increase in broadband penetration

there is an increase in the number of jobs of between 02 percent and 04 percent190 To be conservative we used 03 percent in the middle of the range This leads to a job increase detailed in Table D7 across the six countriesmdashwith an overall increase in jobs at 2 percent in the Base Scenario and 62 percent in the High Scenario

Table D7 Post-integration jobs forecasts for East Africa Base and High Scenarios

Base Scenario Kenya Tanzania Rwanda Uganda South Sudan

Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926

Post integration 17456220 23918836 5972057 18441591 4630436 4837709

Change 313021 576894 102773 519604 52852 60783

Change 2 2 2 3 1 1

High Scenario Kenya Tanzania Rwanda Uganda South Sudan

Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 762848 1801406 500713 1180145 178511 120245

Change 4 8 9 7 4 3190

190 See httpwww2itiforg2013-tech-economy-memopdf citing ITU results


For GDP we have conducted an additional set of panel data econometric regressions based on the work done by Edgardo Sepulveda for the World Bank in the paper titled lsquoBroadband amp Economic Development Regression Analysisrsquo from 2017 The paper studied broadbandrsquos impact based on income level dividing a set of 110 countries into two groups (a) low and medium income and (b) high income To address the well-documented endogeneity issue191 when studying GDP and broadband the paper used a two-stage IV estimation method Specifically the econometric methodology presented in Czernich (2011)192 which uses a nonlinear IV estimation method For additional information on the model and its specifications see Annex D2

For this paper the team used the same two-stage IV estimation method and took a step further with respect to the 2017 paper by dividing the two sets of countries (that is the set of 110 countries from the original paper and the subset of Sub-Saharan countries) depending on penetration levelmdash(a) below 30 percent (b) from 30 percent to 60 percent and (c) above 60 percent) instead of income level As explained in Annex B Sub-Saharan Africa regressions did not show sufficiently significant results due to the limited number of countries with complete data sets for multiple years The team did find significant results (at a 1 percent confidence level) for low and medium broadband penetration levels (30 percent to 60 percent penetration) using the entire set of 110 countries The results suggest that a 10 percent increase in mobile broadband in a country with medium or low penetration increases annual GDP per capita by 080 percent slightly higher than the results obtained in the 2017 paper This leads to a GDP increase detailed in Table D8 across the six countries In total GDP growth due to SDM is estimated to reach US$093 billion meaning a 057 percent increase in average for the Base Scenario and US$26 billion or 16 percent for the High Scenario

191 Ibid


As with jobs these results will be realized when the post-integration changes are achieved with the time horizon set at five years for this study From there the GDP growth rate would increase according to the rate cited earlier and the further increase in broadband penetration and GDP will lead to further increases in jobs

Note also that these results emphasize the positive impacts on the countries currently lagging in online activities suggesting that they will benefit greatly from access to a larger market resulting increase in mobile broadband penetration as well as access to the online content and services already developed in the larger markets On the other hand the more advanced countries such as Kenya have less significant benefit in terms of mobile broadband penetration as their markets are already well-advanced

However the model and resulting analysis do not quantify the significant benefits that existing digital companies will experience given the removal of barriers to serving an expanded regional customer base beyond their national borders As a result for countries with a more advanced digital industry such as Kenya the GDP and job benefits are likely to be higher than measured using this methodology

As shown in Tables D7 and D8 the benefits of integration based on the increase in broadband adoption are positive for both scenarios and will increase over time particularly in those countries that are lagging in broadband penetration today The average increase in GDP for the Base Scenario is 057 percent and 16 percent for the High Scenario while the average increase in employment is 22 percent for the Base Scenario and 62 percent for the High Scenario Across the region the increase in GDP totals US$093 billion and the increase in jobs totals 16 million for the Base Scenario and US$26 billion and 45 million for the High Scenario

69

Table D8 Post-integration GDP forecasts for East Africa Base and High Scenarios


Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007

Change (US$ millions)

343 312 39 197 27 10

Change 049 066 047 077 031 034


Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


836 976 190 448 93 20

Change 119 206 227 176 104 067


Part D Welfare impact for existing broadband users

In the previous sections we have examined the increase in mobile penetration due to integration which brings new users online additionally increasing GDP and jobs However the same factors which lead to growth in the number of mobile broadband users also benefit existing users Existing users benefit from lower prices for the same (or improved) services and from increased network effects which spur the creation of a greater variety and higher quality of digital content and services making internet usage more valuable to them

In this section we seek to estimate the increase in these benefits in terms of the overall increase in consumer surplus which includes some cost savings for the users As price falls consumers save on expenditures Furthermore larger network effects make adoption more attractive for everyone increasing demand which is represented by shifting out the demand curve raising their consumer surplus further as highlighted in the following figures

Figure D4 shows the demand before integration For each country at the particular price level there is a demand for mobile broadband represented based on the intersection of the price and the demand curve In Figure D5 there are two effects First the price falls so that for all existing mobile broadband subscribers there is an expenditure saving represented by the light blue rectangle At the same time the demand curve shifts out to reflect the increased value of being online based on the increased direct and indirect network effects

For existing users this means that their willingness to pay increases as there is more value to being online based on the larger user base in the region as well as the availability of new services that are likely to be made available as a result This increase is represented by the dark blue rectangle The two rectangles together represent the increase in consumer surplus for existing usersmdashthe difference between what they would have been willing to pay based on the demand curve and what they actually pay represented by the lower price level193

193 We note that this increase in consumer surplus is measured conservatively because the dark blue rectangle only represents the increase in willingness to pay for the marginal user For all the other users with a higher willingness to pay there is additional increase in consumer surplus measured as the area between the demand curves However this is difficult to estimate and as shown the increase in consumer surplus is already significant

Figure D4 Mobile broadband adoption demand before integration

Figure D5 Mobile broadband adoption demand after integration

Mobile broadband adoption (quantity)M

ob

ile b

road

ban

d p

rice

Q

PDemand curve (D)

Mobile broadband adoption (quantity)

Mob

ile b

road

band

pric

e

Q

P

Demand curve (D)Drsquo

Prsquo

Prsquorsquo

71

Table D9 provides the numbers for each country both in terms of the gain to individual users in consumer surplus and the aggregate total for all pre-integration subscribers We note that the price impact is most important in the countries with the highest pre-integration prices and that the network effects also have an impact on consumer surplus as countries have access to the large network effects enjoyed by consumers in Kenya as regional barriers are removed This is because of the significance of network effects in the regression results

Existing users in Tanzania and Rwanda gain less because of their relatively low prices pre-integration while those in Uganda South Sudan and Burundi enjoy significant gains because of their

higher pre-integration prices as well as greater network effects Comparably existing users in Kenya with relatively low initial mobile broadband prices and the largest pre-integration network effects (measured by international bandwidth) gain slightly less from integration However it is likely that the benefits in Kenya are underrepresented as the methodology does not account for the increase in new services and the improving quality of services available as a result of the larger regional market In particular over time new innovative content and services will be made available to a much larger regional market Their availability will be reflected by further increases in willingness to pay in all countries including Kenya resulting in further gains in consumer surplus

Table D9 Post-integration increase in consumer surplus for East Africa Base and High

Base Scenario Kenya Tanzania Rwanda Uganda South Sudan Burundi

Consumer surplus gain per sub US$

060 24 27 64 103 53

Total consumer surplus US$ 14937734 387768708 75750421 632018885 107642989 60503986

High Scenario Kenya Tanzania Rwanda Uganda South Sudan Burundi


124 79 87 202 323 167



The gains in consumer surplus are significant in both scenarios due to the price decreases and network effects in all countries except Kenya which already had a significant network and relatively low prices The countries with the highest prices before integration (Uganda and South Sudan) enjoyed the greatest increase in consumer surplus per existing user

Microeconomic impactThis section examines the impact of the SDM on broadband adoption and subsequent economic benefits from increasing broadband penetration at the bottom of the pyramid This review consists of two parts

bull An assessment of distributional impactmdashexploring how the impacts of the SDM particularly with respect to price decreases will be distributed across lower income levels as measured by broadband adoption levels

bull A brief case study of the SDMrsquos impact on access to a key digital service mobile moneymdashexploring the impacts across the economies of East Africa

Adoption levels for lower-income groups

For this review we take advantage of the detailed survey data collected in Brazil by the Regional Center for Studies on the Development of the Information Society This survey has been conducted since 2005 collecting detailed demographic data on households including income levels and information about Internet adoption and usage194 This data set is unique particularly for an emerging economy and provides the best insights into the questions posed in this study Similar data are not available within the East Africa subregion or wider Sub-Saharan Africa

While the data is from Brazil which has a higher per capita income level than countries in East Africa as well as greater levels of fixed broadband it is nevertheless broadly instructive in terms of the likely adoption of broadband in response to service availability and pricing particularly at the base of the pyramid We focus on household adoption data (as opposed to individual usage) as this data is more detailed in terms of family spending on broadband We also use the years 2010ndash2016 because there is no ITU broadband price data available before 2010

Figures D6 and D7 show how overall internet adoption increased 194 For more details see the 2016 Survey at httpswwwcgibrmediadocs

publicacoes2TIC_DOM_2016_LivroEletronicopdf 63 discusses the methodology

at the two ends of the income spectrum covered by the Brazilian data At the lower end in Figure D6 are households with income less than the minimum wage in Brazil (lt1SM ) while Figure D7 presents households with income levels greater than 10 times the minimum wage for Brazil (gt10SM) The lowest income group went from 3 percent to 29 percent internet adoption between 2010 and 2016 while the highest income group went from 86 percent to 97 percent over the same years

Figure D6 Percentage of Brazilian households in the lt1SM segment with Internet access

Figure D7 Percentage of households in the gt10SM segment with Internet access

Source The Brazilian Internet Steering Committee 2017 195

195 See httpswwwcgibrmediadocspublicacoes2TIC_DOM_2016_LivroEletronicopdf

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

73

Over the period covered fixed broadband prices in Brazilian real (R$) have stayed relatively constant as can be seen in Figure D8 At the same time the mobile broadband prices came down relatively significantly in the first year that the ITU measured them and then stayed below the fixed broadband prices While the prices overall look fairly stable the dashed line in Figure D8 shows the increase in GDP per capita in R$ over the same period with a significant increase Thus in terms of affordability against income prices would fall relatively

Figure D8 Fixed and mobile monthly subscription charge (R$) on the left-hand axis and GDP per capita on the right-hand axis (R$)

Source ITU World Bank Analysys Mason 2018

Likely in response to these price differences households at different income levels in Brazil expressed a significant difference in adoption levels of mobile versus fixed broadband As more users came online much of the growth at the lowest income levels came from households taking advantage of mobile broadband offerings As a result by 2016 these households were split relatively evenly between fixed and mobile broadband access while the final dial-up users shifted to broadband (see

Figures D9 and D10) The figure D9 shows the divide among households that have broadband while the one below (Figure D10) shows the divide as a percentage of all households in the lowest income group and therefore demonstrates how mobile broadband adoption drove growth in household adoption

Figure D9 Internet connection type used by households with Internet access (lt1SM)Figure D10 Internet access by connection

-

2000

4000

6000

8000

-

10000

20000

30000

40000

2010 2011 2012 2013 2014 2015 2016

Fixed broadband

Handset - Prepaid 500MB

GDP per capita

type as a of total households in the lt1SM income groupSource ITU World Bank Analysys Mason 2018

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile broadband

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access


This differed significantly from the highest income levels whose growth in adoption came primarily from increased adoption of fixed broadband access over the same period In fact mobile broadband usage in these households declined both as a percentage of households in the income group who were online (see Figure D11) and as a percentage of all households in the income group online (see Figure D12)

Figure D11 Internet connection type used by households with Internet access (gt10SM)

This suggests that increased mobile broadband availability at lower relative prices is a significant driver of broadband adoption in Brazil for lower-income households even when fixed broadband is available In most of Africa including East Africa where mobile broadband is the predominant means of individual and household access this implies that the households at the bottom of the pyramid benefit significantly in terms of affordable online access

In terms of total spending on broadband the trends again differ significantly at different ends of the income spectrum Figure D13 presents the monthly spend on broadband for the lt1SM households At this level the least expensive broadband became relatively more popular shifting up from 10 percent to 24 percent of households By examining the spending patterns of the lowest income group (see Figure D14) as an absolute number of households (as opposed to within the group that has adopted broadband) we can see how as Internet adoption grows over time an increasing number of households take up the lower-cost services We can thus conclude that the lower-cost offerings are driving adoption of internet in the lowest part of the pyramid

Figure D13 Monthly spend on broadband (lt1SM)

Figure D12 Internet access by connection type as a of total households in gt10SM income group


-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

0 25 50 75 100

2010

2011

2012

2013

2014

2015

2016

ltR$30 ltR$31-40 ltR$41-50ltR$51-60 ltR$61-70 ltR$71-80

ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

75

Figure D14 Monthly spend on broadband as a of total households in the lt1SM income group

Source ITU World Bank Analysis Mason 2018

This differs with the highest income level households who tended to shift to the higher price broadband offerings over time as can be seen in Figures D15 and D16 These show that over time the highest priced broadband packages (over R$100) grew both as a percentage of the highest income households online and as a percentage of the growing number of such households online Thus while the lowest income households took advantage of price decreases as a means to go online the highest income households increased adoption of the highest priced packages presumably those with the greatest bandwidth andor data limits


Figure D16 Monthly spend on broadband as a of total households in gt10SM income group


ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 20160 25 50 75 100

2010

2011

2012

2013

2014

2015

2016

Source ITU World Bank Analysys Mason 2018ltR$30 ltR$31-40 ltR$41-50

ltR$51-60 ltR$61-70 ltR$71-80

ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016


The experience of Brazil demonstrates that all users benefit from falling broadband prices but that this impact is disproportionately strong among the lowest-income households which are particularly price sensitive and only able to gain access in significant numbers once low-cost mobile broadband services become available on the market (in contrast to high-cost fixed broadband services)

A similar pattern can be expected in East Africa as a result of the SDM which will help drive the cost of mobile broadband subscriptions below a minimum threshold whereby it becomes affordable to all households driving a disproportionate increase in adoption levels in the lowest income groups previously shut out of the market The impact of the SDM on geographic coverage of mobile broadband in East Africa will likewise enable a larger number of low-income households to come online as mobile broadband coverage spreads to more rural areas

Impact of mobile money

Earlier sections have shown that the SDM will have significant benefits in terms of GDP and jobs growth through an increase in broadband adoption They have also shown that the lower broadband prices that drive much of these increases will affect the bottom of the pyramid in particular These benefits will result from the increased use of online services for a wide range of economic activities While the impact of the SDM on broadband is critical the impact on the availability and quality of online services and how these in turn translate into greater productivity will ultimately be the engine of economic growth

In this subsection we examine the impact of a fundamental online service mobile money The SDM when fully implemented will result in full interoperability and much lower costs to carry out mobile money transfers between proprietary platforms and across borders With transaction costs falling and more consumer choice in terms of the mobile money platform to use adoption of mobile money accounts and transaction volume should increase easing the path toward digitization of the domestic and regional economy Likewise the ability of consumers to easily switch and transact between platforms will drive industry competition

and innovation to improve their services and attract customers rather than relying on market dominance and the stickiness of their customer base The example of Tanzania is illustrative of the regional potential as intra-platform interoperability and strong competition from a range of providers has driven lower prices a rapid uptake of services and launch of innovative new financial products including mobile savings accounts insurance and micro-investment products

Access to mobile money services can have a big impact on poverty reduction The evidence of the impact of the largest mobile money service in Kenya M-PESA is illustrative A recent study has shown that access to M-PESA has lifted 194000 households representing 2 percent of Kenyans out of poverty with greater-than-average benefits for female-headed households The drivers for these benefits are the ability to increase savings receive remittances in times of economic shock and change occupations notably for women to move out of agriculture to business These benefits corresponded to increased proximity to M-PESA agents who now number 110000 across Kenya196 These poverty reduction and job creation impacts would be amplified through an SDM as transaction costs fall transaction volumes increase and the variety as well as quality of digital financial services increases East African citizens in countries such as South Sudan with no significant mobile money presence could reap huge benefits as such services are made available through an SDM

The direct economic benefits of mobile money are only part of the picture Kenya offers other examples of the benefits

bull Agent network As noted there are 110000 M-PESA agents in Kenya whose services drive the benefits of mobile money providing jobs and business opportunities for the agents including in rural areas

bull Complementary services M-PESA has become a platform for other services such as Kopo a business payments and analytics platform Other services have been built on the M-PESA platform such as the ability to pay for M-Kopa solar panels which enables access to electricity for households (particularly those at the bottom of the pyramid who might not otherwise have access)

196 Suri and Jack 2016 ldquoThe long-run poverty and gender impacts of mobile moneyrdquo 1288ndash1292

77

bull Digital payments Finally mobile money services will enable online payments that can help drive e-commerce offering consumer choice while also generating revenue for retailers

As noted the SDM will also allow other mobile money providers the opportunity to compete within the broader regional market As mobile money spreads across East Africa and then becomes more useful through integration these benefits will spread and multiply For instance companies such as M-Kopa can generate scale by making home solar services available across a regional market thereby delivering further benefits while building their business These are examples of the indirect network effects of the broader market as these new services will result in further increases in GDP and jobs as discussed in Section 4 of this paper

ConclusionThe World Bank paper lsquoSingle Digital Market for East Africarsquo outlines a vision and Digital Roadmap to achieve an SDM As defined in the paper the SDM will comprise six countries (that is Burundi Kenya Rwanda South Sudan Tanzania and Uganda) and will aim to eliminate cross-border barriers to the provision of and access to digital infrastructure content and services In addition the SDM will create a seamless competitive regional digital ecosystem that will drive a reinforcing cycle of economic growth investment innovation job creation and improved service delivery Therefore the objective sought by this paper is to estimate the economic impact of deepening regional integration of the telecom market and digital economy across East Africa (that is moving toward an SDM) that will result from implementing the Digital Roadmap and achieving the SDM vision outlined in the main paper

To do so an extensive literature review was conducted and using the ITU GDP Impact General Framework a comprehensive methodology to determine the benefits of an SDM with a focus on East Africa was created In general terms the impact of the SDM consists of lower prices and higher availability of broadband increasing adoption and corresponding network effects which will result in an overall increase in GDP and jobs These benefits will have a significant impact at the bottom of the pyramid where lower broadband prices are set to drive internet adoption the most increasing corresponding

economic and social benefits Meanwhile services such as mobile money will likewise become more accessible to those at the bottom of the pyramid helping to lift these households out of poverty

More specifically the methodology employed has successfully shown that the benefits of integration toward an SDM in East Africa are significant particularly in the countries lagging in broadband availability and adoption The calculations were done for two different scenarios Base and High and the main results are the following

bull In the Base Scenario the average increase in GDP across the region is 057 percentage points five years post-integration or US$093 billion while the average increase in employment is 22 percentage points or approximately 16 million new jobs In the High Scenario GDP increases in the range of 16 percentage points or US$26 billion and the employment increase reaches 62 percentage points or approximately 45 million new jobs In both scenarios these benefits are based on the increase in mobile broadband adoption and subsequent spillover effects resulting from integration

bull Furthermore the paper shows that existing broadband users will also benefit from regional integration as they will pay less for broadband services and will be able to access a wider variety as well as higher value of digital content and services but also to connect with a wider network of users across the region This is defined in the paper as consumer surplus which is significant in both scenarios (US$12 billion in the Base Scenario and US$4 billion in the High Scenario)

bull The paper has described how the experience of Brazil demonstrates that all users benefit from falling broadband prices especially among the lowest-income households and how a similar pattern can be expected in East Africa The SDM will therefore help drive the cost of mobile broadband subscriptions below a minimum threshold whereby it becomes affordable to all households driving a disproportionate increase in adoption levels in the lowest income groups previously shut out of the market

bull Finally as digital services such as mobile money become interoperable and competitive across the region the benefits will further extend to the bottom of the pyramid as more and more citizens gain access to such services for spending earning and producing their own goods and services

These positive estimates however even in the High Scenario are conservative taking much of the economy as constant


Annex D1 Details of econometrics methodology for Part AIn this section we provide a detailed explanation of how we derived the results presented in the main body of the paper Part A of the macroeconomic impacts assessment We will not derive a structural model nor do we have exogenous IVs for price a right-hand variable that is typically endogenous

While the language in this report often implies the existence of a causal relationship between the right-hand variables and penetration we acknowledge a more complicated reality the relationship between the variables probably reflects causal mechanisms running in both directions The goal of this simple exercise is not to demonstrate one-way causality but rather to statistically validate the strength and robustness of an association between the variables197 We believe that the methodology we employ is appropriate

We estimate demand for mobile prepaid Internet service for the 2012ndash2016 period using panel data from all countries in Africa with such data

Given the availability of data we have an unbalanced panel with 157 observations from 41 countries A small number of observations are problematic When we eliminate them we have 148 observations from 36 countries The countries with problematic observations only had one or two observations Most countries remaining in the data set have observations for 4ndash5 years The results are similar with and without the problematic observations Our preferred model excludes these observations We report both sets of results in Table D11 at the end of Annex D1

197 International bandwidth and availability of broadband can be considered exogenous that is they are decisions that take a while to implement (like capacity) Berry Levinson and Pakes (1995) for example assumed that automobile characteristics are exogenous Reference Berry Steven James Levinsohn and Ariel Pakes 1995 ldquoAutomobile Prices in Market Equilibriumrdquo Econometrica 63 (4) 841ndash890

Estimation approach

Having a panel rather than cross-sectional data is advantageous as a cross-section cannot control for time-invariant lsquocountryrsquo effects they are included in the error term in cross-sectional analysis If these unobserved effects are correlated with the right-hand-side variables the estimates from the cross-sectional analysis will be biased however we eliminate this problem by using fixed effects models

The model we employ is noted here as equation (1) Sit = αi + Xitω + εit The variable Sit is the penetration rate for mobile Internet service (denoted as penetration) in country i in year t that is the percentage of the population in country i in year t that has mobile prepaid Internet service

The vector is such that α is a constant and Ai is a vector of unobserved time-invariant country factors The variables in Xit are observable time-varying factors and ω are coefficients to be estimated Finally εit is an error term

There are likely many important unobserved time-invariant project country factors in the vector A Given these unobserved time-invariant project factors equation (1) should be estimated using a fixed effects model in which are parameters to be estimated As Angrist and Pischke (2009)198 note treating α i as a parameter to be estimated is equivalent to estimating in deviations from the mean We tested the alternative to a fixed effects model namely a random effects model The Hausman test strongly rejects the random effects model in favor of a fixed effects model Hence the fixed effects model is appropriate

We summarize the variables heremdasha more detailed description is in the main body of this Annex

Dependent variable (Sit) As noted above Sit is the penetration rate for mobile Internet services (denoted as penetration) in country i in year t that is the percentage of the population in country i in year t that uses mobile Internet services

198 Angrist J and J Pischke 2009 Mostly Harmless Econometrics Princeton New Jersey Princeton University Press

79

Independent var 4 iables (in Xit)

bull Price of mobile prepaid service in US$ in country i in year t

bull International bandwidth in Mbits in country i in year t

bull Availability of broadband (percentage of population covered) in country i in year t

bull Household consumption per capita in US$ in country i in year t

It is very common to estimate demand using a loglog specification where the variables are in natural logarithms and we use that specification The loglog specification has a simple interpretation

Price is typically endogenous and we do not have any IVs at the level of the countryyear199 Hence equation (1) will lead to biased estimates unless one of the following holds

bull The markets for prepaid mobile internet service are competitive

OR

bull Price is uncorrelated with the other right-hand side variables

In the second case the coefficient on price will still be biased (downwards that is toward zero) However the estimates of the coefficients on the other variables will be unbiased

Fortunately it turns out that in our data set price is virtually uncorrelated with international bandwidth in Mbits and household consumption per capita in US dollars In particular the correlation between price and Intl_Bandwidth is minus006 (p value = 040) while the correlation between price and per capita consumption is 009 (p value = 019) Thus the null hypotheses that these correlations equal zero cannot be rejected

The correlation between availability and price is a bit higher minus020 (p value = 003) Nevertheless the correlation is still relatively low between these two variables

Assuming that international bandwidth in Mbits household consumption per capita in US dollars and availability are exogenous to the demand for prepaid mobile service we

199 As discussed earlier international bandwidth and availability are likely endogenous as well but they change slowly over time relative to price and the penetration rate Hence like Berry Levinson and Pakes (1995) we will treat them as exogenous for this study

can estimate equation (1) without price and get lsquoessentiallyrsquo unbiased estimates for the coefficients on international bandwidth in Mbits and household consumption per capita in US dollars

Results

The results of this analysis are shown in Table D11 Regression 1 in Table D11 uses all observations Regression 2 includes all observations but excludes price from the regression Regression 3 which excludes problematic observations is our preferred result Table D11 shows that the estimates on Intl_bandwidth (and price when we include it) are very similar across the regressions Hence the results are particularly robust

In Table D11 all estimated coefficients have the expected sign and all are statistically significant The estimated coefficient on price is negative The estimated coefficients on consumption availability and bandwidth are positive

Despite the potential bias we are interested in the price effect Since the estimate for the coefficient on price is probably biased downwards in absolute value (toward zero) we can use the estimate and be on the conservative side That is that the price effect may be larger than the estimate we obtain

Robustnessbull The estimates are virtually unchanged when we remove

Kenya and South Africa from the analysis These countries have very large amounts of international bandwidth because of the available submarine cable infrastructure and their relatively advanced telecom markets

bull The variable penetration is between zero and one If we use y = [penetration rate (1- penetration rate)] which has a range from [0infin] and then take the natural log and use this variable as the dependent variable the elasticity estimates are quite similar

In summary the results are very robust


Table D11 Fixed effects regressions explaining penetration

REGRESSION 1

All observations Estimates (std error)

REGRESSION 2

(without price) All observations

Estimates (std error)

REGRESSION 3

Without problematic observations


Log of price ndash0070 (0033) ndash0068 (0033)

Log of Intl_Bandwidth 016 (0046) 020 (0037) 015 (0042)

Log of consumption per capita 166 (056) 183 (058) 220 (056)

Log of availability 049 (014) 049 (014) 046 (012)

Observations 157 157 148

Note Dependent variable log of penetration rate p lt 010 p lt 005 p lt 001 We employ robust standard errors (without clustering)

81

Annex D2 Details of econometrics methodology used for Part C

The 2017 Study Regression Analysis of Broadband and Economic Development

This section presents an overview of the study used and its most relevant results which are of relevance to the current paper The regression analysis was based on a comprehensive time-series and cross-country (70 developing and 40 high-income countries) compilation Most previous studies that have focused on fixed broadband in the OECD and EU member countries have found that an increase in fixed broadband penetration had a positive impact on economic growth in the range of 0023 to 0150 The evidence of fixed broadband in developing countries on the other hand has been mixed or inconclusive The few studies looking at mobile broadband penetration have found a positive impact on economic growth in both developing and high-income countries

The statistical methodology used in the 2017 study was based on a well-established growth model to which a two-stage nonlinear IV econometric approach (ldquo2S-IVrdquo) was applied to deal with the well-known reverse causation (endogeneity) challenge In summary simple regression analysis assumes that the ldquodependentrdquo variable in the study (in our case growth of GDP per capita) may be explained by one or more variables (for example mobile broadband penetration) that are ldquoindependentrdquo of the dependent variable That is the flow of causality runs only one way from independent variables to the dependent variable However it is relatively well accepted that telecommunications variables such as broadband penetration affect GDP and are also affected by it If a variable suffers from reverse causation (that is is endogenous) the results of simple regression analysis are likely to be biased That is why a relatively more sophisticated methodology like 2S-IV is preferred

The methodology is based on a simple growth function with constant returns to scale and the three inputs (a) physical capital (b) human capital and (c) labor As further developed in Czernich (2011) the resulting equation to be estimated is as follows

Where is the broadband penetration rate (mobile or fixed) Y is the years since broadband introduction I is the change in investment over GDP E is the growth of the mean years of education Δn is the change in the growth of the working-age population and yt is the GDP per capita for the first year of the sample We refer to the variables for years since broadband introduction and the GDP per capita for the first year of the sample are the lsquoinitial controlsrsquo and are present in every specification The change in investment over GDP the growth of the mean years of education and the change in the growth of the working-age population are referred to as the lsquomacro controlsrsquo

Czernich (2011) used the fixed telephony penetration rate and the cable TV penetration rate to estimate predicted values for fixed broadband penetration rate These predicted values are the instrument and are used in place of the original fixed broadband penetration values in the simple growth function regression There are no available cross-country time-series data for cable TV penetration rates outside the EU and OECD countries so we rely only on the fixed penetration rate to calculate our IV for both high-income and developing countries We adopt this approach to estimate our mobile broadband regressions using mobile telephony penetration rates to estimate the necessary IV (that is the predicted mobile broadband penetration rate)

The regression used to generate the predicted values is a nonlinear diffusion curve that is a dynamic function of year and corresponding voice penetration rates Our first-stage IV regression is a variation on that used by Czernich (2011) which is based on a fixed year (non-dynamic) specification The specification of our first stage of the nonlinear IV as amended from Czernich (2011) is the following


The subscripts i and t identify the country and year of the observation respectively is the broadband penetration rate for country i in year t We estimate the equation presented above and use the results of our estimation to create predicted values for the respective broadband penetration rate for each year in every country These predicted values are essentially the broadband penetration rates controlling for differences between countries and years The predicted values are then used in the second stage of the estimation process By using these predicted values we can attempt to control for any endogeneity between the broadband measure and the independent variable GDP per capita growth

The 2017 studyrsquos results for the impact of mobile broadband in developing countries are relatively robust across base and different full period (2005ndash2015) specifications suggesting that a percentage point increase in the mobile broadband penetration rate increases annual GDP per capita growth by approximately 0056 percentage points The study also presented results for mobile broadband in high-income countries and fixed broadband in developing and high-income countries With a view to consistency with prior studies and other considerations the final results of were based on the 2016 World Banksrsquos GNI per capita-based country classification criteria However such a classification provides only a lsquosnap-shotrsquo of economic conditions In contrast because telecommunications networks are long-lived they are built based on current and long-term expectations of economic conditions As such there may be a lsquomismatchrsquo between current economic conditions and the underlying telecommunications infrastructure and therefore broadband penetration

To explore the sensitivity of the results to such different classification criteria the 2017 study also developed a lsquotelecommunications developmentrsquo country ranking criteria based on a five-year penetration rate ranking for fixed and mobile telephony and broadband penetration The 2017 study also developed a long-term income-based ranking based on the average World Bank GNI per capita country classification for the period from 1995 to 2016

Table D21 shows the key results from the 2017 study that are relevant for the present economic assessment The base regressions for the base sample of 70 developing countries that group together low- and middle-income countries (ldquoLM-70rdquo) are

presented with and without macro controls200 From the total sample of 81 low- and middle-income countries 11 had no or minimal mobile broadband during 2010ndash2015d so were excluded from the regression sample Regression 323 provides significant results201 and is the core regression on which the 0056 multiplier used in the study is based

Table D21 shows Regression 363 which includes a dummy variable for countries in Sub-Saharan Africa as well as Latin America and the Caribbean and confirm that given a particular mobile broadband penetration rate there is an additional negative impact on GDP per capita growth from being in either Latin America and the Caribbean or Sub-Saharan Africa compared to the rest of the world

As noted in the literature overview fixed broadband studies have found a critical mass phenomenon in the EU and OECD countries where the positive impact of broadband only occurs or is enhanced above penetration rates in the range of 15ndash30 percent To see if we can identify such a critical mass phenomenon regression 393 includes interaction terms of the predicted mobile broadband penetration rate for penetration thresholds of 10 percent 35 percent and 65 percent The result indicates that compared to the base mobile broadband effect there is an incrementally negative effect on GDP growth when mobile broadband penetration exceeds 35 percent

Regressions 57-60 and 303-304 represent the results of using the lsquotelecommunications developmentrsquo ranking as the classification criteria to include countries in each of the groups The comparable regression to 323 is 303 which shows a multiplier of 0064 That is higher than the 0056 noted earlier and reflects the results from a different set of countries Note that while 57 shows a positive and significant multiple for the lsquomiddlersquo ranked countries the results for 59 which include the lsquolowrsquo ranked countries are not significant This was a consistent result across all country classification scenarios Lastly regression 313 presents the results of the country classification based on a ranking of the average World Bank categories and shows a multiplier of 0075

200 The lsquowith macrorsquo regressions generally did not provide conclusive results and were not the focus in the current project

201 For all regressions we show the level of significance based on the standard number of asterisks = 1 conf = 5 conf = 10 conf otherwise not significant (NS) We only highlight significant results for the main variable in question and any corresponding variable associated with an extension to the base with red and bold font in the tables

83

Table D21 Selected 2017 study resultsBase (3245) cont (3634) for LM-70 (lowmid-income 2016 (single-year) ranking) Prdquo-series (overall average penetration (low mid amp combined)) LMY-82 (lowmiddle income average 1995ndash2016 ranking)

Regression 323 324 363 364 303 304 57 58 59 60 313 314 393 394

Group LM-70 LM-70 LM-70 LM-70 LMP-66 LMP-66MP-43

MP-43 LP-23 LP-23 LMY-82 LMY-82 LM-70 LM-70

Period 2005ndash2015 2005ndash2015 2004ndash2015 2004ndash2015 2006ndash2015 2005ndash2015

MBB (predicted) 0056 minus0003 0049 0003 0064 minus0032 0075 minus0013 0107 0057 0076 0008 0045 minus0038

Significance NS NS NS NS NS NS NS NS NS

Yrs since mobile BB introduction

minus0006 minus0002 minus0006 minus0002 minus0006 minus0001 minus0001 minus0002 minus0011 minus0002

Significance NS NS

GDP over working ag pop 20032005

minus00002 minus00002 minus00005 minus00004 minus00001

Significance NS NS NS

Macro controls No Yes No Yes No Yes No Yes No Yes No Yes No Yes

Growth iGDP 0031 0035 0013 0041 0031

Significance NS

Growth years educ


Significance NS NS NS NS NS

Change in growth of working age pop



SSA dummy minus0019 minus0022


Significance

LAC dummy minus0017 minus0018

Significance

Predicted MBB gt 10 dummy

0016 0008

Significance NS

Interaction (MBB gt 10 MBB rate)

0067 0027

Significance NS NS


0043 0032

Significance NS NS


minus0129 0075

Significance NS


1537 0178

Significance NS NS


minus2249 minus0264

Significance NS NS

r2 004 003 009 010 007 005 016 013 002 003 006 005 007 004

f-value 1112 453 1544 1016 1895 718 3131 1297 123 116 1817 737 656 278

years 11 11 11 11 12 12 12 12 10 10 11 11 11 11

observations 770 770 770 770 792 792 516 516 230 230 902 902 770 770

Note significance = 1 conf = 5 conf = 10 conf otherwise not significant (NS) MBB = Mobile broadband SSA = Sub-Saharan Africa LAC = Latin America and the Caribbean

85

New 2018 Results

This section presents the results of the new regression analysis carried out for this project It first presents the results based on our initial approach after which the results based on the revised approach are summarized

Initial Sub-Saharan Africa-Centric Approach

The initial approach was based on the observation that the default multiplier of 0056 was based on a ldquoGlobalrdquo sample (LM-70) that included low- and middle-income countries from around the world There are 22 Sub-Saharan African (referred to as lsquoSSA-22rsquo) countries in the LM-70 sample or about 31 percent In this context our initial approach was to attempt to calculate a Sub-Saharan Africa-specific multiplier using only Sub-Saharan Africa data in the sample

Based on our results from the 2017 study however we recognized that the initial approach would not necessarily result in conclusive or reasonable results First we knew from 363 that Sub-Saharan Africa countries had a negative and significant dummy variable Further many Sub-Saharan Africa countries are in the lowest income group 59 and other similar regressions show that low-income regressions generally do not provide significant results (which is why the low- and middle-income groups were combined into LM-70 in the 2017 study The results presented in the following paragraphs confirm these concerns a Sub-Saharan Africa-specific sample applying a 2S-IV methodology did not provide reasonable or significant results Our revised approach is presented in the following section

Table D22 presents the first set of regressions using our initial approach and includes work based on the database compiled for the 2017 study which included data from 2015 (this is referred to as ldquo2017rdquo in the data row of the following tables) as well as the database updated to 2016 for the current project (referred to as ldquo2018rdquo in the data column)

Simple pooled regressions 10 and 11 show a negative but insignificant multiplier Regressions 300-305 and 400ndash415 highlight the problem with the initial approach used presented earlier These include regressions for the original SSA-22 group as well as the expanded SSA-30 group of countries that included the

8 Sub-Saharan African countries that were excluded in the 2017 regressions because they did not meet set mobile broadband penetration thresholds None of the ldquowithout macrosrdquo regressions converge in the first stage of the 2S-IV process which means that an IV cannot be calculated for the second stage Regressions 300Lndash305L address the nonlinear non-convergence by using a linear specification in the first stage Of the regressions that converge the multiplier results are generally negative and sometimes significant

To see whether the above-noted results were robust to specific Sub-Saharan Africa samples regressions 450ndash455 are based on slightly different Sub-Saharan Africa samples that more closely match GDP and mobile broadband penetration of the EAC countries The results were similar to those reported earlier

Regressions 500ndash506 try a different variation this time using the first stage results from LM-70 and applying the predicted IV only for a subsample of Sub-Saharan Africa countries The ldquowithout macrosrdquo results are somewhat encouraging in that they are positive but they are not significant and have relatively low parameter values

At this point we were asking whether the results were reflecting Sub-Saharan Africa-specific factors or more general global phenomenon related to countries with relatively lower penetration or later introduction To test this hypothesis we started to expand the analysis and created a new global sample LM-50 (a subsample of LM-70 that excludes 20 early adopters and high penetration countries) Regressions 310 and 314 are based on LM-50 and have a positive (but insignificant) multipliers in the range of 0019ndash0035

A different quasi Sub-Saharan Africa-centric approach is to focus on the Sub-Saharan Africa-dummy and interaction terms in the context of a global sample Regressions 320 and 330 (for LM-70 and LM-50 for robustness) in Table D24 shows a significant Sub-Saharan Africa interaction of about 0043 to 0045 This is incremental to the corresponding base result which is positive and significant for 320 therefore the Sub-Saharan Africa-specific result multiplier would be the sum of these at 0090 A more conservative approach would take the base as zero (because it is not significant in the robustness check of 330) and suggest that a more conservative Sub-Saharan Africa-specific multiplier is in the range of 0043ndash0045


Table D22 New 2018 regressionsType of Regression

Pooled OLS 2-stage IV (nonlinear) 2-stage IV (linear) 2-stage IV (nonlinear)

Regression 10 11 300 301 304 305 400 401 414 415 300L 301L 304L 305L 310 311 314 315

Data 2017 2018 2017

Group SSA-22 SSA-30 SSA-22 SSA-30 SSA-22 SSA-30 LM-50

Countries SSA Global

Period 2005ndash2015 2007ndash2015

MBB (predicted)

minus0014 minus0006 No minus0016 No 0026 No minus0024 No 0058 minus0090 minus0084 minus0059 minus0048 0035 minus0050 0019 minus0035

Significance NS NS CNV NS CNV NS CNV NS CNV NS NS NS NS NS NS NS

Macro Controls No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes No Yes

Observations 242 242 242 242 330 330 237 237 314 314 242 242 330 330 550 550 450 450

Note Significance = 1 conf = 5 conf = 10 conf otherwise not significant (NS) MBB = Mobile broadband SSA = Sub-Saharan Africa

87


2-stage IV (non-linear)

Overall

regression

500 501 502 504 506

1st stage

regression

123 124 123 123 123

Data 2017

Group LM-70

Countries Global

Period 2005ndash2015

Observations 770

Regression 450 451 452 453 450 454 455 321 300 301 300 300 300

Data 2018 2017

Group SSA-18 SSA-21 SSA-18 SSA-29 LM-70 SSA-22

Countries SSA Global SSA

Period 2008ndash2015 2010ndash2015 2008ndash2015 2005ndash2015 2005ndash2015 2009ndash2015 2010ndash2015 2011ndash2015

MBB

(predicted)

mdash0192 mdash mdash mdash 0064 mdash mdash mdash0001 0017 mdash0003 0007 0020 0012

Significance NS NS NS NS NS NS NS NS NS NS NS

Macro

Controls

No Yes No Yes No No Yes Yes No Yes No No No

Predicted

MBB gt 10

Dummy

mdash-0026

Significance NS

Inter (MBB

gt 10 MBB

rate)

mdash0026

Significance NS

SSA Country

Dummy

-0022

Significance

Inter (SSA

MBB rate)

0039

Significance

Observations 144 130 108 108 144 232 202 770 242 242 154 154 132 132 110



Revised Global Approach

The revised approach reverts to the global sample methodology while applying a project-specific country classification criteria to rank countries and populate the country categories This revised approach also starts to explore whether the multiplier varies by the level of mobile broadband penetration To explore how a project-specific country classification would affect the regression results we created the following ranking criteria based on a weighted average of 2013ndash2015 mobile broadband penetration (ldquoMBBpenrdquo) of the 110 countries in the 2017 study data set (based on 2 3 and 4 weighting)

Weighted Average Mobile BB Penetration = ([2013 MBBpen 2] + [2014 MBBpen 3] + [2015 MBBpen 4])9

In this context the LB-34 group are the 34 countries with lowest rank which coincides with weighted mobile broadband penetration of 0ndash30 percent while MB-39 are the next 39 countries (coinciding with 30ndash60 percent) LMB-73 combines LB-34 and MB-39 The 37 high penetration countries are above 60 percent

Regressions 800ndash805 in Table D25 show that the new classification does have an impact on the regression results Relative to the core regression 323 with 0056 regression 804 has a multiplier that is significant and positive at 0126 Note that as in prior equivalent specifications LB-34 is not significant but MB-39 is positive and significant

Regressions 850ndash852 in Table D24 confirm the earlier Sub-Saharan Africa-specific results they show that even under the most favorable assumptions that general Sub-Saharan Africa-specific multipliers are relatively lowmdashthat is in the range of 0030mdashand not significant

Using the new classification criteria Table D26 examines whether the multiplier varies by level mobile broadband penetration The focus of analysis should be on results from LMB-73 because it has a 30 percent Sub-Saharan Africa weight (compared to MB-39 with about 8 percent) In this regard what regressions 900andash905 do is split the sample by MBBpen ranges and looks at dummies and interaction terms

The dummies and interactions are equivalent to running a series of subsample regressions only for those observations that meet the range for example 900a includes all observations for all 73 countries that are between 0 and 499 percent That would include observations both from a Poland (early days) and Burundi (more recent) Regression 900a (backed by 910a) shows a very strong negative effect for 0ndash5 percent MBBpen This is a large part of the sample 368 of 803 or about 45 percent There is then a big jump in regression 901 to about (0131 + 0116) 0247 (backed by 0258 from 911) for 5ndash25 percent and regression 905 results in a value of (0280ndash0203) 0077 (backed up by Regression 905)

89

Table D24 New 2018 regressions

Type of Regression 2-stage IV (nonlinear)

Overall regression 600 602 604 850 851 852

1st stage regression

Data 2017

Group LM-70 LMB-73

Countries Global


Observations 770 803

Regression 320 322 324 330 332 334 320 322 324 300

Data 2017

Group LM-70 LM-50 LM-70 LM-50 SSA-22

Countries Global SSA

Period 2005ndash2015 2007ndash2015 2009ndash2015 2010ndash15 2005-15 2009-15 2010-15

Mobile BB (predicted) 0047 0058 0053 0003 0022 0010 0002 0005 0002 0038 0031 0011


Macro controls No

SSA country dummy mdash0020 mdash0015 mdash0020 mdash0015 mdash0008 mdash0009

Significance

Inter (SSA MBB rate) 0043 mdash00167 0045 mdash0025 mdash0003 mdash0028





Table D25 New 2018 regressions (based on new classification ranking of average weighted 2013ndash2015 MBBpen


Regression 804 805 802 803 800 801

Data 2017

Group LMB-73 MB-39 LB-34

Countries Global


Mobile BB

(predicted)0126 0119 0110 0114 0001 minus0008

Significance NS NS

Macro controls No

SSA country

dummyminus0019 minus0010 minus0015

Significance NS

Inter (SSA MBB

rate)0034 minus0003 0068




91

Table D26 New 2018 regressions(based on new classification ranking of average weighted 2013ndash2015 MBBpen explore different MBB for LMB-73 and MB-39 [nothing significant for LB-34])


Regression 900a 901 902 903 904a 905 910a 911 912 913 914a 915

Data 2017

Group LMB-73 MB-39

Countries Global


MBB (predicted) 0076 0131 0118 0175 0060 0280 mdash0004 0033 0108 0167 mdash0032 0194

Significance NS NS NS NS

Macro Controls No

Predicted MBB 0-49 Dummy 0013 mdash0010 0077 mdash0013

Significance NS NS

Interaction MBB 0-49 MBB rate) mdash0603 mdash0395 mdash1282 mdash0209

Significance NS

Predicted MBB 5-249 Dummy mdash0014 mdash0029 mdash0050 mdash0073

Significance NS NS

Interaction MBB 5-249 MBB rate) 0116 0175 0258 0287

Significance NS

Predicted MBB 25-449 Dummy 0017 0002 mdash0006 mdash0048


Interaction MBB 25-449 MBB rate)

mdash0033 0024 0031 0100


Predicted MBB 45+ Dummy 0023 0082

Significance NS

Interaction MBB 45+ MBB rate) mdash0103 mdash0180

Significance NS


ignificance NS


Significance NS

Count 368 277 139 19 158


Note Significance = 1 conf = 5 conf = 10 conf otherwise not significant (NS) MBB = Mobile broadband


ConclusionThe literature review included in the 2017 study presented in this annex indicated that broadband generally had a positive impact on GDP growth and that its impact varied between 0023 and 0150 The 0023 and 0150 were relative outliers and the bulk of the results were clustered in the 0040 to 0120 range This is what the literature would consider an academic lsquosafe harborrsquo whose results would generally be considered as reasonable The base specification for low- and middle-income countries in the 2017 study (0056) based on regression 323 was firmly in that range and reflected a conservative stance

In this context the new result of 0126 from regression 804 is just outside the lsquosafe harborrsquo range and could be defensible However it is the highest of a number of our results and one in which we arranged the data (country classification criteria) specifically for our study Further the results generated using our initial approach suggest that a conservative approach is appropriate for application of global results to a specific Sub-Saharan Africa sample In this context the multiplier to be applied to all countries will be an unweighted average of the four above-noted results (0056 0064 0076 0126) this is 0080

93

Annex D3 Sample Countries for Part C

High Upper-Middle Upper-Middle Low

1 Australia Albania Armenia Benin2 Austria Algeria Bangladesh Burkina Faso3 Bahrain Angola Bolivia Burundi4 Belgium Argentina Cambodia Central African Republic5 Canada Azerbaijan Cote drsquoIvoire Chad6 Chile Belarus Egypt Arab Rep Congo Dem Rep7 Croatia Belize El Salvador Gambia The8 Cyprus Bosnia and Herzegovina Ghana Madagascar9 Czech Republic Botswana Honduras Malawi10 Denmark Brazil India Mali11 Estonia Bulgaria Indonesia Mozambique12 Finland China Kenya Niger13 France Colombia Kyrgyz Republic Senegal14 Germany Costa Rica Lao PDR Tanzania15 Greece Ecuador Mauritania Togo16 Hungary Fiji Moldova Uganda17 Iceland Gabon Mongolia Zimbabwe18 Ireland Georgia Morocco19 Italy Guyana Nigeria20 Japan Iran Pakistan21 Korea Rep Jamaica Philippines22 Latvia Jordan Sri Lanka23 Lithuania Kazakhstan Sudan24 Luxembourg Macedonia FYR Swaziland25 Malta Malaysia Tunisia26 Netherlands Mauritius Ukraine27 New Zealand Mexico Uzbekistan28 Norway Namibia29 Oman Panama30 Poland Peru31 Portugal Romania32 Slovak Republic Russian Federation33 Slovenia South Africa34 Spain Suriname35 Sweden Thailand36 Switzerland Turkey37 Trinidad and Tobago Venezuela RB38 United Kingdom39 United States40 Uruguay

Note Countries not included in mobile broadband regressions



SDM EAST AFRICA

Copyright Statement

This work is available under the Creative Commons Attribution Non-Commercial 30 IGO license (CC BY NC 30 IGO) httpcreative-commonsorglicensesby-nc30igo Under the Creative Commons Attribution Non-Commercial license you are free to copy distribute transmit and adapt this work for non-commercial purposes under the following conditions AttributionmdashPlease cite the work as follows World Bank 2018 A Single Digital Market for East Africa - Presenting a joint vision strategic framework roadmap economic impact and readiness assessment copyWorld Bank

License - Creative Commons Attribution Non-Commercial CC 30 IGO Noncommercial - You may not use this work for commercial purposes

Translations - If you create a translation of this work please add the following disclaimer along with the attribution This translation was not created by The World Bank and should not be considered an official World Bank translation The World Bank shall not be liable for any content or error in this translation

Adaptations - If you create an adaptation of this work please add the following disclaimer along with the attribution This is an adaptation of an original work by The World Bank Responsibility for the views and opinions expressed in the adaptation rests solely with the author or authors of the adaptation and are not endorsed by The World Bank

Third-party content - The World Bank does not necessarily own each component of the content contained within the work The World Bank therefore does not warrant that the use of any third-party-owned individual component or part contained in the work will not infringe on the rights of those third parties The risk of claims resulting from such infringement rests solely with you If you wish to re-use a component of the work it is your responsibility to determine whether permission is needed for that re-use and to obtain permission from the copyright owner Examples of components can include but are not limited to tables figures or images

All queries on rights and licenses should be addressed to the Publishing and Knowledge Division The World Bank 1818 H Street NW Washington DC 20433 USA fax 202-522-2625 e-mail pubrightsworldbankorg First edition May 2018

Foreword

Executive Summary

1The building blocks of the Single Digital Market

11Leveraging the lessons learned from global SDM initiatives

12A vision for a Single Digital Market in East Africa

13The SDM strategic framework

14Action andcooperation - towarda lsquoDigital Roadmaprsquo

2Impetus for the Single Digital Market

21Economic and jobs impact

22Drivers of growth and job creation

3Assessment of current digital markets

31A single connectivity market

32A single data market

33Single online market

34Key enablers

4Conclusions and next steps toward implementation

Annex A

Annex B

Annex C

Annex D






Figure 7 Date of entry into force for a selection of agreements and treaties regulatingthe international protection of intellectual property

Figure 8 Share of population that isunregistered









B A SINGLE DIGITAL MARKET FOR EAST AFRICA

A SINGLE DIGITAL MARKET FOR EAST AFRICAPresenting a vision strategic framework implementation roadmap and impact assessment

Casey Torgusson Cecilia Paradi-Guilford Isabella Hayward Ivan Gonzalez Berenguer Pena Edgardo Sepulveda World BankMichael Kende Richard Morgan Neil Gandal David Abecassis Analysys Mason

Copyright copy 2018 International Bank for Reconstruction and Development The World Bank Some rights reserved World Bank 1818 H Street NW Washington DC 20433 Telephone +1 202-473-1000 Internet wwwworldbankorg

Standard Disclaimer

This volume is a product of the staff of The World Bank The findings interpretations and conclusions expressed in this paper do not necessarily reflect the views of the Executive Directors of The World Bank or the governments they represent The World Bank does not guarantee the accuracy of the data included in this work The boundaries colors denominations and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries Nothing herein shall constitute or be considered to be limitation upon or waiver of the privileges and immunities of The World Bank all of which are specifically reserved

This report was prepared by the World Bank in partnership with Analysys Mason Limited supported by a generous grant from the Swedish International Development Agency (Sida)

The report outlines the impetus for creating a single digital market (SDM) in East Africa which would drive deeper integration and spur increased dynamism of the digital economies of six East African countries Burundi Kenya Rwanda South Sudan Tanzania and Uganda

The content of this report has greatly benefited from comprehensive regional stakeholder consultation with the governments of the six countries as well as the private sector civil society organizations development partners and academia

Multi-stakeholder workshops took place in Kenya Rwanda Tanzania and Uganda between April and May 2017 The research team has also gathered inputs through a series of bilateral meetings and a qualitative online survey

These consultations along with desk research have helped shape a joint vision and strategic framework for the SDM presented in this report including an assessment of the state of existing markets

Acknowledgments This report was researched and prepared by a team from the World Bank Group and the consulting firm Analysys Mason Limited

Analysys Mason Limited North West Wing Bush House Aldwych London WC2B 4PJ Telephone +44 (0)20 7395 9000 Internetwwwanalysysmasoncom Registered in England No 5177472

The team would like to thank the following for sharing their feedback on the report which helped strengthen the presentation and analysis Paul Noumba Um Coordinating Country Director in Charge of Regional Integration Boutheina Guermazi Practice Manager for Digital Development Tim Kelly Lead ICT Policy Specialist Deo Ndikumana Senior Operations Officer for Regional Integration Vijay Pillai Advisor Africa Region Yutaka Yoshino Program Lead and Lead Economist for Equitable Growth Finance and Institutions Yoko Doi Senior Financial Specialist and Eva Clemente Miranda Private Sector Specialist

SDM EAST AFRICA

SDM EAST AFRICA


Content

Foreword iv

Executive Summary 1













34 Key enablers 32






Figures





















Foreword


Executive Summary
























SINGLE CONNECTIVITY

MARKET

Action












SINGLE DATA MARKET

Action






Action




ENABLING REGIONAL

ENVIRONMENT

Action

















SDM SCORECARD

POLICY TOOLS

DIGITAL ROADMAP

INVESTMENT

TARGET INDICATORS

ENABLING ENVIROMENT


BURUNDI

KENYA

TANZANIA

UGANDA

SOUTH SUDAN

RWANDA

SINGLE ONLINEMARKET

SINGLE DATAMARKET



Introduction

































DIG

ITAL

SKIL


INNO

VATI

ON

ENVI

RONM

ENT

HARD

INFR

Arsquo(E

GP

OW

ER)

DIG

ITAL

LEAD

ERSH

IP

CAPI

TAL

FINA

NCIN

G


SINGLEONLINEMARKET

SINGLE DATAMARKET




















































SINGLE CONNECTIVITY

MARKET

Action











SINGLE DATA MARKET

Action








Action




ENABLING REGIONAL

ENVIRONMENT

Action


























1400

1200

1000

800

600

400

200

200 M20

0 M

CH

INA

IND

IA

E27

USA

IND

ON

ESIA

BR

AZI

L

PA

KIS

TAN

NIG

ERIA

EAST

AFR

ICA

EAST AFRICAB

AN

GLA

DES

H

RU

SIA

MEX

ICO

JAP

AN

PH

ILIP

PIN

ES

TANZ

ANIA

KENY

A

UGAN

DA

SOUT

H SU

DAN

RWAN

DA

BURU

NDI

0

200

0

POPU

LATI

ON

(MIL

LLIO

N)











SINGLE DATA MARKET









O

F PO

PULA

TIO

N B

ASED

ON

SU

BSC

RIPT

ION

S

0

5

10

15

20

25

30

35

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA52 67

13

20219

262 262

92

27

337

34

02 03 0311

82

0 0

Internetusage rates

Mobilebroadband

Fixedbroadband














C

OVE

R BY

PO

PULA

TIO

N

0

20

40

60

80

100

120

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

2G 3G 4G



































0

10

20

30

40

50

60

70

80

90

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Operator 1

Operator 5

Operator 2

Operator 6

Operator 3

Operator 7

Operator 4

Operator 8



0

10

20

30

40

50

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F G

NI P

C

223

44 44 44

392

141

261

354

10

175

279

500MB 1G










Source ITU 2017























Paris Convention

Berne Convention


Madrid Protocol




Rwanda 1984 1984 2011 2013 ucirc





177 176 152 101 96



















42 See wwwawherecom

























0

20

40

60

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Total Population


O

F PE

OPL

E TH

AT A

RE U

NRE

GIS

TERE

D

53

44

27

49

24

47

19 20 18

40

30











Source Findex 2017






0

20

40

60

80

SOU

THSU

DAN

BURU

NDI

TAN

ZAN

IA

UG

ANDA

KEN

YA

RWAN

DA



O

F AD

ULT

S O

VER

THE

AGE

OF

15

7

56

73

37

31

9

21

3933

51

1




Africell Money uuml


BK mVISA uuml

Dau-Pesa uuml

EcoKash uuml

EzeeMoney uuml

ezyPesa uuml

Leo Manoti uuml

M-PESA uuml

mHose uuml

M-Sente uuml

mCash uuml

Micropay uuml



Orange Money uuml

PAYG Platform uuml

Tangaza Pesa uuml

Tigo Cash uuml

Tigo Pesa uuml

Vodacom M-pesa uuml































0102030405060708090

100

UG

ANDA

BURU

NDI

TAN

ZAN

IA

KEN

YA

RWAN

DA2016

90 85 93 91 91

6979

6982

972014

0

02

04

06

08

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

AFRI

CA

EURO

PE

KENY

A

RWAN

DA

023018

034 034029

072

036042







76 Ibid




















0

50

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F PO

PULA

TIO

N76 89

328

56

294 267

























Rwanda 56 41 2

Kenya 92 80 3

Uganda 115 122 12

Tanzania 132 137 15

Burundi 157 164 34














102 See httpsklabrw



















0

20

10

30

40

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

1

16

6

30

16

1

4 7

2018

2016
















0

20

100

40

60

80

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

85

78 74

32

71

80



























Annex A










Mediumndashlong 1st











Regional


Short 2nd


Regional Long 2nd





Regional


Medium 2nd





Regional Medium 1st


Regional


Medium 2nd



Regional


Short 1st







Regional


Short 1st


SINGLE DATA MARKET





Short 1st


Regional


Medium 2nd



Regional


Medium 1st





Regional Medium 2nd




Mediumndashlong 1st


Regional


MediumndashLong 2nd









Regional






Medium 2nd




Short 2nd




Medium 1st



Regional Short 2nd


National Medium 1st



Regional


long (traditional)

2nd


Regional Long 2nd











Mediumndashlong 1st









Medium 2nd







Medium 1st





Regional


Long 2nd








Regional Short 2nd






Medium 2nd





National






Regional


Mediumndashlong 2nd




Short 1st






45

Annex B

SDM Scorecard











Framework Category



Tanzania Uganda



172

(148)

138

(291

153

(218)nd

165

(181)

152

(219) (7)



(GSMA 2017)127

(ITU 2016)

nd

53

95

94

100

100

nd

30

89

95

nd

9197



(ITU 2017)128

(GSMA 2017)129

40

40

78

80

921

939

20

20

85

305

64

4590



(GSMA 2017)130 107 25 643 nd 277 45 75



Connectivity


48 831 699 ~215 744 551 90



(World Bank 2016)1311 11 6 0 2 2 15






~61 690 75 04 ~17 55 90



(ITU 2017)


~52

9

~26

90

~20

37

~67

nd

~13

40

~219

4970




83 262 27 11 92 337 55




223

279

44

44

44

392

141

261

3

54

10

175

1

15





130 Ibid


47

Framework Category



Tanzania Uganda

Connectivity


0 03 02 0 34 03

Connectivity


09 21 02 0 19 19 30



Data


Data



26 37 53 nd 32 33 55

Data


Online services


Online services




1 73 31 nd 39 51 90



4 79 39 7 43 55 85




133 Ibid





138 Ibid



Framework Category



Tanzania Uganda






173

(023)

119

(042)

138

(034)

183

(018)

130

(035)

128

(036) 065



15 days


days2

days







37 43 44 nd 28 31 50



3 5 43 nd 4 43 55





26 29 34 nd 24 24 4

140 Ibid





145 Ibid

146 Ibid

147 Ibid


49

Framework Category



Tanzania Uganda







Above 100



122

(2131)

80

(3095

99

(2736)nd

96

(2797)

102

(2697)



24 43 47 nd 36 33 5





24 41 44 nd 31 34 55




150 Ibid

151 Ibid

















(ITU 2017)158 (GSMA 2017)159

40

40

78

80

921

939

20

20

85

305

64

45







nd 293 0 1355 442 344 No more than 50














51




(UNCTAD 2018)161






In progress

Yes YesIn





No

Travel only (not

digital ID)

Travel


No No

Travel

only

(not digital ID)










(UNCTAD 2018)162








161 Ibid

162 Ibid

163 Ibid












37 34 35 nd 28 9 None



4702 6763 7244 2619 2021 628 80








165 Ibid - page 13


53

Annex C









































55



















































Single data market

bull Cybersecurity


bull E-services









57




































Annex D


59







Source ITU














MICUS (2008)


Koutroumpis (2009)


Czernich (2011)




ITU (2012)





BROADBANDDEPLOYMENT

DIRECTBENEFITS



TOTALFACTOR

PRODUCTIVITY

HOUSEHOLDINCOME

CONSUMERSURPLUS

CONTRIBUTIONTO GDP

GROWTH


DEPLOYMENT




















61










Methodology









Part Objective




Part D





63


Variable Definition

Dependent variable
















Variable

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


5171 2913 2384 2384 854 1095


246 230 254 584 931 483


860570 12190 19024 49984 352 3510


82 85 92 45 20 40






Results187



015


220





65








Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


2 2 2 2 2 2


881835 36570 57072 149952 1056 10530


100 100 100 56 25 50

High Scenario

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


15 15 15 15 15 15


903100 60950 95120 249920 1760 17550


100 100 100 90 68 69





Burundi


52 29 24 24 9 11


58 37 30 34 12 15



Burundi


52 29 24 24 9 11


67 55 52 46 22 19





67






Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 313021 576894 102773 519604 52852 60783

Change 2 2 2 3 1 1


Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 762848 1801406 500713 1180145 178511 120245

Change 4 8 9 7 4 3190





191 Ibid






69



Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


343 312 39 197 27 10

Change 049 066 047 077 031 034


Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


836 976 190 448 93 20

Change 119 206 227 176 104 067











ob

ile b

road

ban

d p

rice

Q

PDemand curve (D)


Mob

ile b

road

band

pric

e

Q

P


Prsquo

Prsquorsquo

71







060 24 27 64 103 53




124 79 87 202 323 167











publicacoes2TIC_DOM_2016_LivroEletronicopdf Page 163 discusses the methodology






-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

73







-

2000

4000

6000

8000

-

10000

20000

30000

40000

2010 2011 2012 2013 2014 2015 2016

Fixed broadband


GDP per capita


-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile broadband

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access









-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

0 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

75







ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 20160 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$51-60 ltR$61-70 ltR$71-80

ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016













77


















Estimation approach








79









OR








Results










REGRESSION 1


REGRESSION 2



REGRESSION 3









81




















83


Regression 323 324 363 364 303 304 57 58 59 60 313 314 393 394








Significance NS NS





Growth iGDP 0031 0035 0013 0041 0031

Significance NS

Growth years educ








Significance


Significance


0016 0008

Significance NS


0067 0027

Significance NS NS


0043 0032

Significance NS NS


minus0129 0075

Significance NS


1537 0178

Significance NS NS


minus2249 minus0264

Significance NS NS

r2 004 003 009 010 007 005 016 013 002 003 006 005 007 004

f-value 1112 453 1544 1016 1895 718 3131 1297 123 116 1817 737 656 278

years 11 11 11 11 12 12 12 12 10 10 11 11 11 11

observations 770 770 770 770 792 792 516 516 230 230 902 902 770 770


85

New 2018 Results















Regression 10 11 300 301 304 305 400 401 414 415 300L 301L 304L 305L 310 311 314 315

Data 2017 2018 2017




MBB (predicted)




Observations 242 242 242 242 330 330 237 237 314 314 242 242 330 330 550 550 450 450


87



Overall

regression

500 501 502 504 506

1st stage

regression

123 124 123 123 123

Data 2017

Group LM-70

Countries Global


Observations 770

Regression 450 451 452 453 450 454 455 321 300 301 300 300 300

Data 2018 2017




MBB

(predicted)



Macro

Controls


Predicted

MBB gt 10

Dummy

mdash-0026

Significance NS

Inter (MBB

gt 10 MBB

rate)

mdash0026

Significance NS

SSA Country

Dummy

-0022

Significance

Inter (SSA

MBB rate)

0039

Significance

Observations 144 130 108 108 144 232 202 770 242 242 154 154 132 132 110











89





Data 2017

Group LM-70 LMB-73

Countries Global



Regression 320 322 324 330 332 334 320 322 324 300

Data 2017






Macro controls No


Significance








Regression 804 805 802 803 800 801

Data 2017


Countries Global


Mobile BB

(predicted)0126 0119 0110 0114 0001 minus0008

Significance NS NS

Macro controls No

SSA country


Significance NS

Inter (SSA MBB

rate)0034 minus0003 0068




91



Regression 900a 901 902 903 904a 905 910a 911 912 913 914a 915

Data 2017

Group LMB-73 MB-39

Countries Global




Macro Controls No


Significance NS NS


Significance NS


Significance NS NS


Significance NS




mdash0033 0024 0031 0100



Significance NS


Significance NS


ignificance NS


Significance NS

Count 368 277 139 19 158






93







SDM EAST AFRICA

Copyright Statement







Foreword

Executive Summary













34Key enablers


Annex A

Annex B

Annex C

Annex D

















Content

Foreword iv

Executive Summary 1













34 Key enablers 32






Figures





















Foreword


Executive Summary
























SINGLE CONNECTIVITY

MARKET

Action












SINGLE DATA MARKET

Action






Action




ENABLING REGIONAL

ENVIRONMENT

Action

















SDM SCORECARD

POLICY TOOLS

DIGITAL ROADMAP

INVESTMENT

TARGET INDICATORS

ENABLING ENVIROMENT


BURUNDI

KENYA

TANZANIA

UGANDA

SOUTH SUDAN

RWANDA

SINGLE ONLINEMARKET

SINGLE DATAMARKET



Introduction

































DIG

ITAL

SKIL


INNO

VATI

ON

ENVI

RONM

ENT

HARD

INFR

Arsquo(E

GP

OW

ER)

DIG

ITAL

LEAD

ERSH

IP

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TAL

FINA

NCIN

G


SINGLEONLINEMARKET

SINGLE DATAMARKET




















































SINGLE CONNECTIVITY

MARKET

Action











SINGLE DATA MARKET

Action








Action




ENABLING REGIONAL

ENVIRONMENT

Action


























1400

1200

1000

800

600

400

200

200 M20

0 M

CH

INA

IND

IA

E27

USA

IND

ON

ESIA

BR

AZI

L

PA

KIS

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ERIA

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ICA

EAST AFRICAB

AN

GLA

DES

H

RU

SIA

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ICO

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AN

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ES

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A

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DA

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DA

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0

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ON

(MIL

LLIO

N)











SINGLE DATA MARKET









O

F PO

PULA

TIO

N B

ASED

ON

SU

BSC

RIPT

ION

S

0

5

10

15

20

25

30

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H SU

DAN

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TANZ

ANIA

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DA

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A

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DA52 67

13

20219

262 262

92

27

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34

02 03 0311

82

0 0

Internetusage rates

Mobilebroadband

Fixedbroadband














C

OVE

R BY

PO

PULA

TIO

N

0

20

40

60

80

100

120

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HSU

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DA

2G 3G 4G



































0

10

20

30

40

50

60

70

80

90

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

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DA

KENY

A

RWAN

DA

Operator 1

Operator 5

Operator 2

Operator 6

Operator 3

Operator 7

Operator 4

Operator 8



0

10

20

30

40

50

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H SU

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DA

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DA

O

F G

NI P

C

223

44 44 44

392

141

261

354

10

175

279

500MB 1G










Source ITU 2017























Paris Convention

Berne Convention


Madrid Protocol




Rwanda 1984 1984 2011 2013 ucirc





177 176 152 101 96



















42 See wwwawherecom

























0

20

40

60

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Total Population


O

F PE

OPL

E TH

AT A

RE U

NRE

GIS

TERE

D

53

44

27

49

24

47

19 20 18

40

30











Source Findex 2017






0

20

40

60

80

SOU

THSU

DAN

BURU

NDI

TAN

ZAN

IA

UG

ANDA

KEN

YA

RWAN

DA



O

F AD

ULT

S O

VER

THE

AGE

OF

15

7

56

73

37

31

9

21

3933

51

1




Africell Money uuml


BK mVISA uuml

Dau-Pesa uuml

EcoKash uuml

EzeeMoney uuml

ezyPesa uuml

Leo Manoti uuml

M-PESA uuml

mHose uuml

M-Sente uuml

mCash uuml

Micropay uuml



Orange Money uuml

PAYG Platform uuml

Tangaza Pesa uuml

Tigo Cash uuml

Tigo Pesa uuml

Vodacom M-pesa uuml































0102030405060708090

100

UG

ANDA

BURU

NDI

TAN

ZAN

IA

KEN

YA

RWAN

DA2016

90 85 93 91 91

6979

6982

972014

0

02

04

06

08

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

AFRI

CA

EURO

PE

KENY

A

RWAN

DA

023018

034 034029

072

036042







76 Ibid




















0

50

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F PO

PULA

TIO

N76 89

328

56

294 267

























Rwanda 56 41 2

Kenya 92 80 3

Uganda 115 122 12

Tanzania 132 137 15

Burundi 157 164 34














102 See httpsklabrw



















0

20

10

30

40

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

1

16

6

30

16

1

4 7

2018

2016
















0

20

100

40

60

80

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

85

78 74

32

71

80



























Annex A










Mediumndashlong 1st











Regional


Short 2nd


Regional Long 2nd





Regional


Medium 2nd





Regional Medium 1st


Regional


Medium 2nd



Regional


Short 1st







Regional


Short 1st


SINGLE DATA MARKET





Short 1st


Regional


Medium 2nd



Regional


Medium 1st





Regional Medium 2nd




Mediumndashlong 1st


Regional


MediumndashLong 2nd









Regional






Medium 2nd




Short 2nd




Medium 1st



Regional Short 2nd


National Medium 1st



Regional


long (traditional)

2nd


Regional Long 2nd











Mediumndashlong 1st









Medium 2nd







Medium 1st





Regional


Long 2nd








Regional Short 2nd






Medium 2nd





National






Regional


Mediumndashlong 2nd




Short 1st






45

Annex B

SDM Scorecard











Framework Category



Tanzania Uganda



172

(148)

138

(291

153

(218)nd

165

(181)

152

(219) (7)



(GSMA 2017)127

(ITU 2016)

nd

53

95

94

100

100

nd

30

89

95

nd

9197



(ITU 2017)128

(GSMA 2017)129

40

40

78

80

921

939

20

20

85

305

64

4590



(GSMA 2017)130 107 25 643 nd 277 45 75



Connectivity


48 831 699 ~215 744 551 90



(World Bank 2016)1311 11 6 0 2 2 15






~61 690 75 04 ~17 55 90



(ITU 2017)


~52

9

~26

90

~20

37

~67

nd

~13

40

~219

4970




83 262 27 11 92 337 55




223

279

44

44

44

392

141

261

3

54

10

175

1

15





130 Ibid


47

Framework Category



Tanzania Uganda

Connectivity


0 03 02 0 34 03

Connectivity


09 21 02 0 19 19 30



Data


Data



26 37 53 nd 32 33 55

Data


Online services


Online services




1 73 31 nd 39 51 90



4 79 39 7 43 55 85




133 Ibid





138 Ibid



Framework Category



Tanzania Uganda






173

(023)

119

(042)

138

(034)

183

(018)

130

(035)

128

(036) 065



15 days


days2

days







37 43 44 nd 28 31 50



3 5 43 nd 4 43 55





26 29 34 nd 24 24 4

140 Ibid





145 Ibid

146 Ibid

147 Ibid


49

Framework Category



Tanzania Uganda







Above 100



122

(2131)

80

(3095

99

(2736)nd

96

(2797)

102

(2697)



24 43 47 nd 36 33 5





24 41 44 nd 31 34 55




150 Ibid

151 Ibid

















(ITU 2017)158 (GSMA 2017)159

40

40

78

80

921

939

20

20

85

305

64

45







nd 293 0 1355 442 344 No more than 50














51




(UNCTAD 2018)161






In progress

Yes YesIn





No

Travel only (not

digital ID)

Travel


No No

Travel

only

(not digital ID)










(UNCTAD 2018)162








161 Ibid

162 Ibid

163 Ibid












37 34 35 nd 28 9 None



4702 6763 7244 2619 2021 628 80








165 Ibid - page 13


53

Annex C









































55



















































Single data market

bull Cybersecurity


bull E-services









57




































Annex D


59







Source ITU














MICUS (2008)


Koutroumpis (2009)


Czernich (2011)




ITU (2012)





BROADBANDDEPLOYMENT

DIRECTBENEFITS



TOTALFACTOR

PRODUCTIVITY

HOUSEHOLDINCOME

CONSUMERSURPLUS

CONTRIBUTIONTO GDP

GROWTH


DEPLOYMENT




















61










Methodology









Part Objective




Part D





63


Variable Definition

Dependent variable
















Variable

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


5171 2913 2384 2384 854 1095


246 230 254 584 931 483


860570 12190 19024 49984 352 3510


82 85 92 45 20 40






Results187



015


220





65








Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


2 2 2 2 2 2


881835 36570 57072 149952 1056 10530


100 100 100 56 25 50

High Scenario

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


15 15 15 15 15 15


903100 60950 95120 249920 1760 17550


100 100 100 90 68 69





Burundi


52 29 24 24 9 11


58 37 30 34 12 15



Burundi


52 29 24 24 9 11


67 55 52 46 22 19





67






Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 313021 576894 102773 519604 52852 60783

Change 2 2 2 3 1 1


Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 762848 1801406 500713 1180145 178511 120245

Change 4 8 9 7 4 3190





191 Ibid






69



Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


343 312 39 197 27 10

Change 049 066 047 077 031 034


Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


836 976 190 448 93 20

Change 119 206 227 176 104 067











ob

ile b

road

ban

d p

rice

Q

PDemand curve (D)


Mob

ile b

road

band

pric

e

Q

P


Prsquo

Prsquorsquo

71







060 24 27 64 103 53




124 79 87 202 323 167

















-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

73







-

2000

4000

6000

8000

-

10000

20000

30000

40000

2010 2011 2012 2013 2014 2015 2016

Fixed broadband


GDP per capita


-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile broadband

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access









-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

0 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

75







ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 20160 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$51-60 ltR$61-70 ltR$71-80

ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016













77


















Estimation approach








79









OR








Results










REGRESSION 1


REGRESSION 2



REGRESSION 3









81




















83


Regression 323 324 363 364 303 304 57 58 59 60 313 314 393 394








Significance NS NS





Growth iGDP 0031 0035 0013 0041 0031

Significance NS

Growth years educ








Significance


Significance


0016 0008

Significance NS


0067 0027

Significance NS NS


0043 0032

Significance NS NS


minus0129 0075

Significance NS


1537 0178

Significance NS NS


minus2249 minus0264

Significance NS NS

r2 004 003 009 010 007 005 016 013 002 003 006 005 007 004

f-value 1112 453 1544 1016 1895 718 3131 1297 123 116 1817 737 656 278

years 11 11 11 11 12 12 12 12 10 10 11 11 11 11

observations 770 770 770 770 792 792 516 516 230 230 902 902 770 770


85

New 2018 Results















Regression 10 11 300 301 304 305 400 401 414 415 300L 301L 304L 305L 310 311 314 315

Data 2017 2018 2017




MBB (predicted)




Observations 242 242 242 242 330 330 237 237 314 314 242 242 330 330 550 550 450 450


87



Overall

regression

500 501 502 504 506

1st stage

regression

123 124 123 123 123

Data 2017

Group LM-70

Countries Global


Observations 770

Regression 450 451 452 453 450 454 455 321 300 301 300 300 300

Data 2018 2017




MBB

(predicted)



Macro

Controls


Predicted

MBB gt 10

Dummy

mdash-0026

Significance NS

Inter (MBB

gt 10 MBB

rate)

mdash0026

Significance NS

SSA Country

Dummy

-0022

Significance

Inter (SSA

MBB rate)

0039

Significance

Observations 144 130 108 108 144 232 202 770 242 242 154 154 132 132 110











89





Data 2017

Group LM-70 LMB-73

Countries Global



Regression 320 322 324 330 332 334 320 322 324 300

Data 2017






Macro controls No


Significance








Regression 804 805 802 803 800 801

Data 2017


Countries Global


Mobile BB

(predicted)0126 0119 0110 0114 0001 minus0008

Significance NS NS

Macro controls No

SSA country


Significance NS

Inter (SSA MBB

rate)0034 minus0003 0068




91



Regression 900a 901 902 903 904a 905 910a 911 912 913 914a 915

Data 2017

Group LMB-73 MB-39

Countries Global




Macro Controls No


Significance NS NS


Significance NS


Significance NS NS


Significance NS




mdash0033 0024 0031 0100



Significance NS


Significance NS


ignificance NS


Significance NS

Count 368 277 139 19 158






93







SDM EAST AFRICA

Copyright Statement







Foreword

Executive Summary













34Key enablers


Annex A

Annex B

Annex C

Annex D





















Foreword


Executive Summary
























SINGLE CONNECTIVITY

MARKET

Action












SINGLE DATA MARKET

Action






Action




ENABLING REGIONAL

ENVIRONMENT

Action

















SDM SCORECARD

POLICY TOOLS

DIGITAL ROADMAP

INVESTMENT

TARGET INDICATORS

ENABLING ENVIROMENT


BURUNDI

KENYA

TANZANIA

UGANDA

SOUTH SUDAN

RWANDA

SINGLE ONLINEMARKET

SINGLE DATAMARKET



Introduction

































DIG

ITAL

SKIL


INNO

VATI

ON

ENVI

RONM

ENT

HARD

INFR

Arsquo(E

GP

OW

ER)

DIG

ITAL

LEAD

ERSH

IP

CAPI

TAL

FINA

NCIN

G


SINGLEONLINEMARKET

SINGLE DATAMARKET




















































SINGLE CONNECTIVITY

MARKET

Action











SINGLE DATA MARKET

Action








Action




ENABLING REGIONAL

ENVIRONMENT

Action


























1400

1200

1000

800

600

400

200

200 M20

0 M

CH

INA

IND

IA

E27

USA

IND

ON

ESIA

BR

AZI

L

PA

KIS

TAN

NIG

ERIA

EAST

AFR

ICA

EAST AFRICAB

AN

GLA

DES

H

RU

SIA

MEX

ICO

JAP

AN

PH

ILIP

PIN

ES

TANZ

ANIA

KENY

A

UGAN

DA

SOUT

H SU

DAN

RWAN

DA

BURU

NDI

0

200

0

POPU

LATI

ON

(MIL

LLIO

N)











SINGLE DATA MARKET









O

F PO

PULA

TIO

N B

ASED

ON

SU

BSC

RIPT

ION

S

0

5

10

15

20

25

30

35

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA52 67

13

20219

262 262

92

27

337

34

02 03 0311

82

0 0

Internetusage rates

Mobilebroadband

Fixedbroadband














C

OVE

R BY

PO

PULA

TIO

N

0

20

40

60

80

100

120

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

2G 3G 4G



































0

10

20

30

40

50

60

70

80

90

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Operator 1

Operator 5

Operator 2

Operator 6

Operator 3

Operator 7

Operator 4

Operator 8



0

10

20

30

40

50

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F G

NI P

C

223

44 44 44

392

141

261

354

10

175

279

500MB 1G










Source ITU 2017























Paris Convention

Berne Convention


Madrid Protocol




Rwanda 1984 1984 2011 2013 ucirc





177 176 152 101 96



















42 See wwwawherecom

























0

20

40

60

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Total Population


O

F PE

OPL

E TH

AT A

RE U

NRE

GIS

TERE

D

53

44

27

49

24

47

19 20 18

40

30











Source Findex 2017






0

20

40

60

80

SOU

THSU

DAN

BURU

NDI

TAN

ZAN

IA

UG

ANDA

KEN

YA

RWAN

DA



O

F AD

ULT

S O

VER

THE

AGE

OF

15

7

56

73

37

31

9

21

3933

51

1




Africell Money uuml


BK mVISA uuml

Dau-Pesa uuml

EcoKash uuml

EzeeMoney uuml

ezyPesa uuml

Leo Manoti uuml

M-PESA uuml

mHose uuml

M-Sente uuml

mCash uuml

Micropay uuml



Orange Money uuml

PAYG Platform uuml

Tangaza Pesa uuml

Tigo Cash uuml

Tigo Pesa uuml

Vodacom M-pesa uuml































0102030405060708090

100

UG

ANDA

BURU

NDI

TAN

ZAN

IA

KEN

YA

RWAN

DA2016

90 85 93 91 91

6979

6982

972014

0

02

04

06

08

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

AFRI

CA

EURO

PE

KENY

A

RWAN

DA

023018

034 034029

072

036042







76 Ibid




















0

50

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F PO

PULA

TIO

N76 89

328

56

294 267

























Rwanda 56 41 2

Kenya 92 80 3

Uganda 115 122 12

Tanzania 132 137 15

Burundi 157 164 34














102 See httpsklabrw



















0

20

10

30

40

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

1

16

6

30

16

1

4 7

2018

2016
















0

20

100

40

60

80

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

85

78 74

32

71

80



























Annex A










Mediumndashlong 1st











Regional


Short 2nd


Regional Long 2nd





Regional


Medium 2nd





Regional Medium 1st


Regional


Medium 2nd



Regional


Short 1st







Regional


Short 1st


SINGLE DATA MARKET





Short 1st


Regional


Medium 2nd



Regional


Medium 1st





Regional Medium 2nd




Mediumndashlong 1st


Regional


MediumndashLong 2nd









Regional






Medium 2nd




Short 2nd




Medium 1st



Regional Short 2nd


National Medium 1st



Regional


long (traditional)

2nd


Regional Long 2nd











Mediumndashlong 1st









Medium 2nd







Medium 1st





Regional


Long 2nd








Regional Short 2nd






Medium 2nd





National






Regional


Mediumndashlong 2nd




Short 1st






45

Annex B

SDM Scorecard











Framework Category



Tanzania Uganda



172

(148)

138

(291

153

(218)nd

165

(181)

152

(219) (7)



(GSMA 2017)127

(ITU 2016)

nd

53

95

94

100

100

nd

30

89

95

nd

9197



(ITU 2017)128

(GSMA 2017)129

40

40

78

80

921

939

20

20

85

305

64

4590



(GSMA 2017)130 107 25 643 nd 277 45 75



Connectivity


48 831 699 ~215 744 551 90



(World Bank 2016)1311 11 6 0 2 2 15






~61 690 75 04 ~17 55 90



(ITU 2017)


~52

9

~26

90

~20

37

~67

nd

~13

40

~219

4970




83 262 27 11 92 337 55




223

279

44

44

44

392

141

261

3

54

10

175

1

15





130 Ibid


47

Framework Category



Tanzania Uganda

Connectivity


0 03 02 0 34 03

Connectivity


09 21 02 0 19 19 30



Data


Data



26 37 53 nd 32 33 55

Data


Online services


Online services




1 73 31 nd 39 51 90



4 79 39 7 43 55 85




133 Ibid





138 Ibid



Framework Category



Tanzania Uganda






173

(023)

119

(042)

138

(034)

183

(018)

130

(035)

128

(036) 065



15 days


days2

days







37 43 44 nd 28 31 50



3 5 43 nd 4 43 55





26 29 34 nd 24 24 4

140 Ibid





145 Ibid

146 Ibid

147 Ibid


49

Framework Category



Tanzania Uganda







Above 100



122

(2131)

80

(3095

99

(2736)nd

96

(2797)

102

(2697)



24 43 47 nd 36 33 5





24 41 44 nd 31 34 55




150 Ibid

151 Ibid

















(ITU 2017)158 (GSMA 2017)159

40

40

78

80

921

939

20

20

85

305

64

45







nd 293 0 1355 442 344 No more than 50














51




(UNCTAD 2018)161






In progress

Yes YesIn





No

Travel only (not

digital ID)

Travel


No No

Travel

only

(not digital ID)










(UNCTAD 2018)162








161 Ibid

162 Ibid

163 Ibid












37 34 35 nd 28 9 None



4702 6763 7244 2619 2021 628 80








165 Ibid - page 13


53

Annex C









































55



















































Single data market

bull Cybersecurity


bull E-services









57




































Annex D


59







Source ITU














MICUS (2008)


Koutroumpis (2009)


Czernich (2011)




ITU (2012)





BROADBANDDEPLOYMENT

DIRECTBENEFITS



TOTALFACTOR

PRODUCTIVITY

HOUSEHOLDINCOME

CONSUMERSURPLUS

CONTRIBUTIONTO GDP

GROWTH


DEPLOYMENT




















61










Methodology









Part Objective




Part D





63


Variable Definition

Dependent variable
















Variable

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


5171 2913 2384 2384 854 1095


246 230 254 584 931 483


860570 12190 19024 49984 352 3510


82 85 92 45 20 40






Results187



015


220





65








Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


2 2 2 2 2 2


881835 36570 57072 149952 1056 10530


100 100 100 56 25 50

High Scenario

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


15 15 15 15 15 15


903100 60950 95120 249920 1760 17550


100 100 100 90 68 69





Burundi


52 29 24 24 9 11


58 37 30 34 12 15



Burundi


52 29 24 24 9 11


67 55 52 46 22 19





67






Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 313021 576894 102773 519604 52852 60783

Change 2 2 2 3 1 1


Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 762848 1801406 500713 1180145 178511 120245

Change 4 8 9 7 4 3190





191 Ibid






69



Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


343 312 39 197 27 10

Change 049 066 047 077 031 034


Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


836 976 190 448 93 20

Change 119 206 227 176 104 067











ob

ile b

road

ban

d p

rice

Q

PDemand curve (D)


Mob

ile b

road

band

pric

e

Q

P


Prsquo

Prsquorsquo

71







060 24 27 64 103 53




124 79 87 202 323 167

















-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

73







-

2000

4000

6000

8000

-

10000

20000

30000

40000

2010 2011 2012 2013 2014 2015 2016

Fixed broadband


GDP per capita


-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile broadband

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access









-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

0 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

75







ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 20160 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$51-60 ltR$61-70 ltR$71-80

ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016













77


















Estimation approach








79









OR








Results










REGRESSION 1


REGRESSION 2



REGRESSION 3









81




















83


Regression 323 324 363 364 303 304 57 58 59 60 313 314 393 394








Significance NS NS





Growth iGDP 0031 0035 0013 0041 0031

Significance NS

Growth years educ








Significance


Significance


0016 0008

Significance NS


0067 0027

Significance NS NS


0043 0032

Significance NS NS


minus0129 0075

Significance NS


1537 0178

Significance NS NS


minus2249 minus0264

Significance NS NS

r2 004 003 009 010 007 005 016 013 002 003 006 005 007 004

f-value 1112 453 1544 1016 1895 718 3131 1297 123 116 1817 737 656 278

years 11 11 11 11 12 12 12 12 10 10 11 11 11 11

observations 770 770 770 770 792 792 516 516 230 230 902 902 770 770


85

New 2018 Results















Regression 10 11 300 301 304 305 400 401 414 415 300L 301L 304L 305L 310 311 314 315

Data 2017 2018 2017




MBB (predicted)




Observations 242 242 242 242 330 330 237 237 314 314 242 242 330 330 550 550 450 450


87



Overall

regression

500 501 502 504 506

1st stage

regression

123 124 123 123 123

Data 2017

Group LM-70

Countries Global


Observations 770

Regression 450 451 452 453 450 454 455 321 300 301 300 300 300

Data 2018 2017




MBB

(predicted)



Macro

Controls


Predicted

MBB gt 10

Dummy

mdash-0026

Significance NS

Inter (MBB

gt 10 MBB

rate)

mdash0026

Significance NS

SSA Country

Dummy

-0022

Significance

Inter (SSA

MBB rate)

0039

Significance

Observations 144 130 108 108 144 232 202 770 242 242 154 154 132 132 110











89





Data 2017

Group LM-70 LMB-73

Countries Global



Regression 320 322 324 330 332 334 320 322 324 300

Data 2017






Macro controls No


Significance








Regression 804 805 802 803 800 801

Data 2017


Countries Global


Mobile BB

(predicted)0126 0119 0110 0114 0001 minus0008

Significance NS NS

Macro controls No

SSA country


Significance NS

Inter (SSA MBB

rate)0034 minus0003 0068




91



Regression 900a 901 902 903 904a 905 910a 911 912 913 914a 915

Data 2017

Group LMB-73 MB-39

Countries Global




Macro Controls No


Significance NS NS


Significance NS


Significance NS NS


Significance NS




mdash0033 0024 0031 0100



Significance NS


Significance NS


ignificance NS


Significance NS

Count 368 277 139 19 158






93







SDM EAST AFRICA

Copyright Statement







Foreword

Executive Summary













34Key enablers


Annex A

Annex B

Annex C

Annex D

















Executive Summary
























SINGLE CONNECTIVITY

MARKET

Action












SINGLE DATA MARKET

Action






Action




ENABLING REGIONAL

ENVIRONMENT

Action

















SDM SCORECARD

POLICY TOOLS

DIGITAL ROADMAP

INVESTMENT

TARGET INDICATORS

ENABLING ENVIROMENT


BURUNDI

KENYA

TANZANIA

UGANDA

SOUTH SUDAN

RWANDA

SINGLE ONLINEMARKET

SINGLE DATAMARKET



Introduction

































DIG

ITAL

SKIL


INNO

VATI

ON

ENVI

RONM

ENT

HARD

INFR

Arsquo(E

GP

OW

ER)

DIG

ITAL

LEAD

ERSH

IP

CAPI

TAL

FINA

NCIN

G


SINGLEONLINEMARKET

SINGLE DATAMARKET




















































SINGLE CONNECTIVITY

MARKET

Action











SINGLE DATA MARKET

Action








Action




ENABLING REGIONAL

ENVIRONMENT

Action


























1400

1200

1000

800

600

400

200

200 M20

0 M

CH

INA

IND

IA

E27

USA

IND

ON

ESIA

BR

AZI

L

PA

KIS

TAN

NIG

ERIA

EAST

AFR

ICA

EAST AFRICAB

AN

GLA

DES

H

RU

SIA

MEX

ICO

JAP

AN

PH

ILIP

PIN

ES

TANZ

ANIA

KENY

A

UGAN

DA

SOUT

H SU

DAN

RWAN

DA

BURU

NDI

0

200

0

POPU

LATI

ON

(MIL

LLIO

N)











SINGLE DATA MARKET









O

F PO

PULA

TIO

N B

ASED

ON

SU

BSC

RIPT

ION

S

0

5

10

15

20

25

30

35

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA52 67

13

20219

262 262

92

27

337

34

02 03 0311

82

0 0

Internetusage rates

Mobilebroadband

Fixedbroadband














C

OVE

R BY

PO

PULA

TIO

N

0

20

40

60

80

100

120

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

2G 3G 4G



































0

10

20

30

40

50

60

70

80

90

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Operator 1

Operator 5

Operator 2

Operator 6

Operator 3

Operator 7

Operator 4

Operator 8



0

10

20

30

40

50

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F G

NI P

C

223

44 44 44

392

141

261

354

10

175

279

500MB 1G










Source ITU 2017























Paris Convention

Berne Convention


Madrid Protocol




Rwanda 1984 1984 2011 2013 ucirc





177 176 152 101 96



















42 See wwwawherecom

























0

20

40

60

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Total Population


O

F PE

OPL

E TH

AT A

RE U

NRE

GIS

TERE

D

53

44

27

49

24

47

19 20 18

40

30











Source Findex 2017






0

20

40

60

80

SOU

THSU

DAN

BURU

NDI

TAN

ZAN

IA

UG

ANDA

KEN

YA

RWAN

DA



O

F AD

ULT

S O

VER

THE

AGE

OF

15

7

56

73

37

31

9

21

3933

51

1




Africell Money uuml


BK mVISA uuml

Dau-Pesa uuml

EcoKash uuml

EzeeMoney uuml

ezyPesa uuml

Leo Manoti uuml

M-PESA uuml

mHose uuml

M-Sente uuml

mCash uuml

Micropay uuml



Orange Money uuml

PAYG Platform uuml

Tangaza Pesa uuml

Tigo Cash uuml

Tigo Pesa uuml

Vodacom M-pesa uuml































0102030405060708090

100

UG

ANDA

BURU

NDI

TAN

ZAN

IA

KEN

YA

RWAN

DA2016

90 85 93 91 91

6979

6982

972014

0

02

04

06

08

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

AFRI

CA

EURO

PE

KENY

A

RWAN

DA

023018

034 034029

072

036042







76 Ibid




















0

50

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F PO

PULA

TIO

N76 89

328

56

294 267

























Rwanda 56 41 2

Kenya 92 80 3

Uganda 115 122 12

Tanzania 132 137 15

Burundi 157 164 34














102 See httpsklabrw



















0

20

10

30

40

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

1

16

6

30

16

1

4 7

2018

2016
















0

20

100

40

60

80

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

85

78 74

32

71

80



























Annex A










Mediumndashlong 1st











Regional


Short 2nd


Regional Long 2nd





Regional


Medium 2nd





Regional Medium 1st


Regional


Medium 2nd



Regional


Short 1st







Regional


Short 1st


SINGLE DATA MARKET





Short 1st


Regional


Medium 2nd



Regional


Medium 1st





Regional Medium 2nd




Mediumndashlong 1st


Regional


MediumndashLong 2nd









Regional






Medium 2nd




Short 2nd




Medium 1st



Regional Short 2nd


National Medium 1st



Regional


long (traditional)

2nd


Regional Long 2nd











Mediumndashlong 1st









Medium 2nd







Medium 1st





Regional


Long 2nd








Regional Short 2nd






Medium 2nd





National






Regional


Mediumndashlong 2nd




Short 1st






45

Annex B

SDM Scorecard











Framework Category



Tanzania Uganda



172

(148)

138

(291

153

(218)nd

165

(181)

152

(219) (7)



(GSMA 2017)127

(ITU 2016)

nd

53

95

94

100

100

nd

30

89

95

nd

9197



(ITU 2017)128

(GSMA 2017)129

40

40

78

80

921

939

20

20

85

305

64

4590



(GSMA 2017)130 107 25 643 nd 277 45 75



Connectivity


48 831 699 ~215 744 551 90



(World Bank 2016)1311 11 6 0 2 2 15






~61 690 75 04 ~17 55 90



(ITU 2017)


~52

9

~26

90

~20

37

~67

nd

~13

40

~219

4970




83 262 27 11 92 337 55




223

279

44

44

44

392

141

261

3

54

10

175

1

15





130 Ibid


47

Framework Category



Tanzania Uganda

Connectivity


0 03 02 0 34 03

Connectivity


09 21 02 0 19 19 30



Data


Data



26 37 53 nd 32 33 55

Data


Online services


Online services




1 73 31 nd 39 51 90



4 79 39 7 43 55 85




133 Ibid





138 Ibid



Framework Category



Tanzania Uganda






173

(023)

119

(042)

138

(034)

183

(018)

130

(035)

128

(036) 065



15 days


days2

days







37 43 44 nd 28 31 50



3 5 43 nd 4 43 55





26 29 34 nd 24 24 4

140 Ibid





145 Ibid

146 Ibid

147 Ibid


49

Framework Category



Tanzania Uganda







Above 100



122

(2131)

80

(3095

99

(2736)nd

96

(2797)

102

(2697)



24 43 47 nd 36 33 5





24 41 44 nd 31 34 55




150 Ibid

151 Ibid

















(ITU 2017)158 (GSMA 2017)159

40

40

78

80

921

939

20

20

85

305

64

45







nd 293 0 1355 442 344 No more than 50














51




(UNCTAD 2018)161






In progress

Yes YesIn





No

Travel only (not

digital ID)

Travel


No No

Travel

only

(not digital ID)










(UNCTAD 2018)162








161 Ibid

162 Ibid

163 Ibid












37 34 35 nd 28 9 None



4702 6763 7244 2619 2021 628 80








165 Ibid - page 13


53

Annex C









































55



















































Single data market

bull Cybersecurity


bull E-services









57




































Annex D


59







Source ITU














MICUS (2008)


Koutroumpis (2009)


Czernich (2011)




ITU (2012)





BROADBANDDEPLOYMENT

DIRECTBENEFITS



TOTALFACTOR

PRODUCTIVITY

HOUSEHOLDINCOME

CONSUMERSURPLUS

CONTRIBUTIONTO GDP

GROWTH


DEPLOYMENT




















61










Methodology









Part Objective




Part D





63


Variable Definition

Dependent variable
















Variable

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


5171 2913 2384 2384 854 1095


246 230 254 584 931 483


860570 12190 19024 49984 352 3510


82 85 92 45 20 40






Results187



015


220





65








Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


2 2 2 2 2 2


881835 36570 57072 149952 1056 10530


100 100 100 56 25 50

High Scenario

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


15 15 15 15 15 15


903100 60950 95120 249920 1760 17550


100 100 100 90 68 69





Burundi


52 29 24 24 9 11


58 37 30 34 12 15



Burundi


52 29 24 24 9 11


67 55 52 46 22 19





67






Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 313021 576894 102773 519604 52852 60783

Change 2 2 2 3 1 1


Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 762848 1801406 500713 1180145 178511 120245

Change 4 8 9 7 4 3190





191 Ibid






69



Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


343 312 39 197 27 10

Change 049 066 047 077 031 034


Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


836 976 190 448 93 20

Change 119 206 227 176 104 067











ob

ile b

road

ban

d p

rice

Q

PDemand curve (D)


Mob

ile b

road

band

pric

e

Q

P


Prsquo

Prsquorsquo

71







060 24 27 64 103 53




124 79 87 202 323 167

















-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

73







-

2000

4000

6000

8000

-

10000

20000

30000

40000

2010 2011 2012 2013 2014 2015 2016

Fixed broadband


GDP per capita


-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile broadband

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access









-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

0 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

75







ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 20160 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$51-60 ltR$61-70 ltR$71-80

ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016













77


















Estimation approach








79









OR








Results










REGRESSION 1


REGRESSION 2



REGRESSION 3









81




















83


Regression 323 324 363 364 303 304 57 58 59 60 313 314 393 394








Significance NS NS





Growth iGDP 0031 0035 0013 0041 0031

Significance NS

Growth years educ








Significance


Significance


0016 0008

Significance NS


0067 0027

Significance NS NS


0043 0032

Significance NS NS


minus0129 0075

Significance NS


1537 0178

Significance NS NS


minus2249 minus0264

Significance NS NS

r2 004 003 009 010 007 005 016 013 002 003 006 005 007 004

f-value 1112 453 1544 1016 1895 718 3131 1297 123 116 1817 737 656 278

years 11 11 11 11 12 12 12 12 10 10 11 11 11 11

observations 770 770 770 770 792 792 516 516 230 230 902 902 770 770


85

New 2018 Results















Regression 10 11 300 301 304 305 400 401 414 415 300L 301L 304L 305L 310 311 314 315

Data 2017 2018 2017




MBB (predicted)




Observations 242 242 242 242 330 330 237 237 314 314 242 242 330 330 550 550 450 450


87



Overall

regression

500 501 502 504 506

1st stage

regression

123 124 123 123 123

Data 2017

Group LM-70

Countries Global


Observations 770

Regression 450 451 452 453 450 454 455 321 300 301 300 300 300

Data 2018 2017




MBB

(predicted)



Macro

Controls


Predicted

MBB gt 10

Dummy

mdash-0026

Significance NS

Inter (MBB

gt 10 MBB

rate)

mdash0026

Significance NS

SSA Country

Dummy

-0022

Significance

Inter (SSA

MBB rate)

0039

Significance

Observations 144 130 108 108 144 232 202 770 242 242 154 154 132 132 110











89





Data 2017

Group LM-70 LMB-73

Countries Global



Regression 320 322 324 330 332 334 320 322 324 300

Data 2017






Macro controls No


Significance








Regression 804 805 802 803 800 801

Data 2017


Countries Global


Mobile BB

(predicted)0126 0119 0110 0114 0001 minus0008

Significance NS NS

Macro controls No

SSA country


Significance NS

Inter (SSA MBB

rate)0034 minus0003 0068




91



Regression 900a 901 902 903 904a 905 910a 911 912 913 914a 915

Data 2017

Group LMB-73 MB-39

Countries Global




Macro Controls No


Significance NS NS


Significance NS


Significance NS NS


Significance NS




mdash0033 0024 0031 0100



Significance NS


Significance NS


ignificance NS


Significance NS

Count 368 277 139 19 158






93







SDM EAST AFRICA

Copyright Statement







Foreword

Executive Summary













34Key enablers


Annex A

Annex B

Annex C

Annex D

























SINGLE CONNECTIVITY

MARKET

Action












SINGLE DATA MARKET

Action






Action




ENABLING REGIONAL

ENVIRONMENT

Action

















SDM SCORECARD

POLICY TOOLS

DIGITAL ROADMAP

INVESTMENT

TARGET INDICATORS

ENABLING ENVIROMENT


BURUNDI

KENYA

TANZANIA

UGANDA

SOUTH SUDAN

RWANDA

SINGLE ONLINEMARKET

SINGLE DATAMARKET



Introduction

































DIG

ITAL

SKIL


INNO

VATI

ON

ENVI

RONM

ENT

HARD

INFR

Arsquo(E

GP

OW

ER)

DIG

ITAL

LEAD

ERSH

IP

CAPI

TAL

FINA

NCIN

G


SINGLEONLINEMARKET

SINGLE DATAMARKET




















































SINGLE CONNECTIVITY

MARKET

Action











SINGLE DATA MARKET

Action








Action




ENABLING REGIONAL

ENVIRONMENT

Action


























1400

1200

1000

800

600

400

200

200 M20

0 M

CH

INA

IND

IA

E27

USA

IND

ON

ESIA

BR

AZI

L

PA

KIS

TAN

NIG

ERIA

EAST

AFR

ICA

EAST AFRICAB

AN

GLA

DES

H

RU

SIA

MEX

ICO

JAP

AN

PH

ILIP

PIN

ES

TANZ

ANIA

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A

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DA

SOUT

H SU

DAN

RWAN

DA

BURU

NDI

0

200

0

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LATI

ON

(MIL

LLIO

N)











SINGLE DATA MARKET









O

F PO

PULA

TIO

N B

ASED

ON

SU

BSC

RIPT

ION

S

0

5

10

15

20

25

30

35

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA52 67

13

20219

262 262

92

27

337

34

02 03 0311

82

0 0

Internetusage rates

Mobilebroadband

Fixedbroadband














C

OVE

R BY

PO

PULA

TIO

N

0

20

40

60

80

100

120

SOUT

HSU

DAN

BURU

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TANZ

ANIA

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DA

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A

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DA

2G 3G 4G



































0

10

20

30

40

50

60

70

80

90

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Operator 1

Operator 5

Operator 2

Operator 6

Operator 3

Operator 7

Operator 4

Operator 8



0

10

20

30

40

50

SOUT

H SU

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BURU

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TANZ

ANIA

UGAN

DA

KENY

A

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DA

O

F G

NI P

C

223

44 44 44

392

141

261

354

10

175

279

500MB 1G










Source ITU 2017























Paris Convention

Berne Convention


Madrid Protocol




Rwanda 1984 1984 2011 2013 ucirc





177 176 152 101 96



















42 See wwwawherecom

























0

20

40

60

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Total Population


O

F PE

OPL

E TH

AT A

RE U

NRE

GIS

TERE

D

53

44

27

49

24

47

19 20 18

40

30











Source Findex 2017






0

20

40

60

80

SOU

THSU

DAN

BURU

NDI

TAN

ZAN

IA

UG

ANDA

KEN

YA

RWAN

DA



O

F AD

ULT

S O

VER

THE

AGE

OF

15

7

56

73

37

31

9

21

3933

51

1




Africell Money uuml


BK mVISA uuml

Dau-Pesa uuml

EcoKash uuml

EzeeMoney uuml

ezyPesa uuml

Leo Manoti uuml

M-PESA uuml

mHose uuml

M-Sente uuml

mCash uuml

Micropay uuml



Orange Money uuml

PAYG Platform uuml

Tangaza Pesa uuml

Tigo Cash uuml

Tigo Pesa uuml

Vodacom M-pesa uuml































0102030405060708090

100

UG

ANDA

BURU

NDI

TAN

ZAN

IA

KEN

YA

RWAN

DA2016

90 85 93 91 91

6979

6982

972014

0

02

04

06

08

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

AFRI

CA

EURO

PE

KENY

A

RWAN

DA

023018

034 034029

072

036042







76 Ibid




















0

50

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F PO

PULA

TIO

N76 89

328

56

294 267

























Rwanda 56 41 2

Kenya 92 80 3

Uganda 115 122 12

Tanzania 132 137 15

Burundi 157 164 34














102 See httpsklabrw



















0

20

10

30

40

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

1

16

6

30

16

1

4 7

2018

2016
















0

20

100

40

60

80

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

85

78 74

32

71

80



























Annex A










Mediumndashlong 1st











Regional


Short 2nd


Regional Long 2nd





Regional


Medium 2nd





Regional Medium 1st


Regional


Medium 2nd



Regional


Short 1st







Regional


Short 1st


SINGLE DATA MARKET





Short 1st


Regional


Medium 2nd



Regional


Medium 1st





Regional Medium 2nd




Mediumndashlong 1st


Regional


MediumndashLong 2nd









Regional






Medium 2nd




Short 2nd




Medium 1st



Regional Short 2nd


National Medium 1st



Regional


long (traditional)

2nd


Regional Long 2nd











Mediumndashlong 1st









Medium 2nd







Medium 1st





Regional


Long 2nd








Regional Short 2nd






Medium 2nd





National






Regional


Mediumndashlong 2nd




Short 1st






45

Annex B

SDM Scorecard











Framework Category



Tanzania Uganda



172

(148)

138

(291

153

(218)nd

165

(181)

152

(219) (7)



(GSMA 2017)127

(ITU 2016)

nd

53

95

94

100

100

nd

30

89

95

nd

9197



(ITU 2017)128

(GSMA 2017)129

40

40

78

80

921

939

20

20

85

305

64

4590



(GSMA 2017)130 107 25 643 nd 277 45 75



Connectivity


48 831 699 ~215 744 551 90



(World Bank 2016)1311 11 6 0 2 2 15






~61 690 75 04 ~17 55 90



(ITU 2017)


~52

9

~26

90

~20

37

~67

nd

~13

40

~219

4970




83 262 27 11 92 337 55




223

279

44

44

44

392

141

261

3

54

10

175

1

15





130 Ibid


47

Framework Category



Tanzania Uganda

Connectivity


0 03 02 0 34 03

Connectivity


09 21 02 0 19 19 30



Data


Data



26 37 53 nd 32 33 55

Data


Online services


Online services




1 73 31 nd 39 51 90



4 79 39 7 43 55 85




133 Ibid





138 Ibid



Framework Category



Tanzania Uganda






173

(023)

119

(042)

138

(034)

183

(018)

130

(035)

128

(036) 065



15 days


days2

days







37 43 44 nd 28 31 50



3 5 43 nd 4 43 55





26 29 34 nd 24 24 4

140 Ibid





145 Ibid

146 Ibid

147 Ibid


49

Framework Category



Tanzania Uganda







Above 100



122

(2131)

80

(3095

99

(2736)nd

96

(2797)

102

(2697)



24 43 47 nd 36 33 5





24 41 44 nd 31 34 55




150 Ibid

151 Ibid

















(ITU 2017)158 (GSMA 2017)159

40

40

78

80

921

939

20

20

85

305

64

45







nd 293 0 1355 442 344 No more than 50














51




(UNCTAD 2018)161






In progress

Yes YesIn





No

Travel only (not

digital ID)

Travel


No No

Travel

only

(not digital ID)










(UNCTAD 2018)162








161 Ibid

162 Ibid

163 Ibid












37 34 35 nd 28 9 None



4702 6763 7244 2619 2021 628 80








165 Ibid - page 13


53

Annex C









































55



















































Single data market

bull Cybersecurity


bull E-services









57




































Annex D


59







Source ITU














MICUS (2008)


Koutroumpis (2009)


Czernich (2011)




ITU (2012)





BROADBANDDEPLOYMENT

DIRECTBENEFITS



TOTALFACTOR

PRODUCTIVITY

HOUSEHOLDINCOME

CONSUMERSURPLUS

CONTRIBUTIONTO GDP

GROWTH


DEPLOYMENT




















61










Methodology









Part Objective




Part D





63


Variable Definition

Dependent variable
















Variable

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


5171 2913 2384 2384 854 1095


246 230 254 584 931 483


860570 12190 19024 49984 352 3510


82 85 92 45 20 40






Results187



015


220





65








Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


2 2 2 2 2 2


881835 36570 57072 149952 1056 10530


100 100 100 56 25 50

High Scenario

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


15 15 15 15 15 15


903100 60950 95120 249920 1760 17550


100 100 100 90 68 69





Burundi


52 29 24 24 9 11


58 37 30 34 12 15



Burundi


52 29 24 24 9 11


67 55 52 46 22 19





67






Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 313021 576894 102773 519604 52852 60783

Change 2 2 2 3 1 1


Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 762848 1801406 500713 1180145 178511 120245

Change 4 8 9 7 4 3190





191 Ibid






69



Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


343 312 39 197 27 10

Change 049 066 047 077 031 034


Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


836 976 190 448 93 20

Change 119 206 227 176 104 067











ob

ile b

road

ban

d p

rice

Q

PDemand curve (D)


Mob

ile b

road

band

pric

e

Q

P


Prsquo

Prsquorsquo

71







060 24 27 64 103 53




124 79 87 202 323 167

















-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

73







-

2000

4000

6000

8000

-

10000

20000

30000

40000

2010 2011 2012 2013 2014 2015 2016

Fixed broadband


GDP per capita


-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile broadband

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access









-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

0 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

75







ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 20160 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$51-60 ltR$61-70 ltR$71-80

ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016













77


















Estimation approach








79









OR








Results










REGRESSION 1


REGRESSION 2



REGRESSION 3









81




















83


Regression 323 324 363 364 303 304 57 58 59 60 313 314 393 394








Significance NS NS





Growth iGDP 0031 0035 0013 0041 0031

Significance NS

Growth years educ








Significance


Significance


0016 0008

Significance NS


0067 0027

Significance NS NS


0043 0032

Significance NS NS


minus0129 0075

Significance NS


1537 0178

Significance NS NS


minus2249 minus0264

Significance NS NS

r2 004 003 009 010 007 005 016 013 002 003 006 005 007 004

f-value 1112 453 1544 1016 1895 718 3131 1297 123 116 1817 737 656 278

years 11 11 11 11 12 12 12 12 10 10 11 11 11 11

observations 770 770 770 770 792 792 516 516 230 230 902 902 770 770


85

New 2018 Results















Regression 10 11 300 301 304 305 400 401 414 415 300L 301L 304L 305L 310 311 314 315

Data 2017 2018 2017




MBB (predicted)




Observations 242 242 242 242 330 330 237 237 314 314 242 242 330 330 550 550 450 450


87



Overall

regression

500 501 502 504 506

1st stage

regression

123 124 123 123 123

Data 2017

Group LM-70

Countries Global


Observations 770

Regression 450 451 452 453 450 454 455 321 300 301 300 300 300

Data 2018 2017




MBB

(predicted)



Macro

Controls


Predicted

MBB gt 10

Dummy

mdash-0026

Significance NS

Inter (MBB

gt 10 MBB

rate)

mdash0026

Significance NS

SSA Country

Dummy

-0022

Significance

Inter (SSA

MBB rate)

0039

Significance

Observations 144 130 108 108 144 232 202 770 242 242 154 154 132 132 110











89





Data 2017

Group LM-70 LMB-73

Countries Global



Regression 320 322 324 330 332 334 320 322 324 300

Data 2017






Macro controls No


Significance








Regression 804 805 802 803 800 801

Data 2017


Countries Global


Mobile BB

(predicted)0126 0119 0110 0114 0001 minus0008

Significance NS NS

Macro controls No

SSA country


Significance NS

Inter (SSA MBB

rate)0034 minus0003 0068




91



Regression 900a 901 902 903 904a 905 910a 911 912 913 914a 915

Data 2017

Group LMB-73 MB-39

Countries Global




Macro Controls No


Significance NS NS


Significance NS


Significance NS NS


Significance NS




mdash0033 0024 0031 0100



Significance NS


Significance NS


ignificance NS


Significance NS

Count 368 277 139 19 158






93







SDM EAST AFRICA

Copyright Statement







Foreword

Executive Summary













34Key enablers


Annex A

Annex B

Annex C

Annex D

















SINGLE DATA MARKET

Action






Action




ENABLING REGIONAL

ENVIRONMENT

Action

















SDM SCORECARD

POLICY TOOLS

DIGITAL ROADMAP

INVESTMENT

TARGET INDICATORS

ENABLING ENVIROMENT


BURUNDI

KENYA

TANZANIA

UGANDA

SOUTH SUDAN

RWANDA

SINGLE ONLINEMARKET

SINGLE DATAMARKET



Introduction

































DIG

ITAL

SKIL


INNO

VATI

ON

ENVI

RONM

ENT

HARD

INFR

Arsquo(E

GP

OW

ER)

DIG

ITAL

LEAD

ERSH

IP

CAPI

TAL

FINA

NCIN

G


SINGLEONLINEMARKET

SINGLE DATAMARKET




















































SINGLE CONNECTIVITY

MARKET

Action











SINGLE DATA MARKET

Action








Action




ENABLING REGIONAL

ENVIRONMENT

Action


























1400

1200

1000

800

600

400

200

200 M20

0 M

CH

INA

IND

IA

E27

USA

IND

ON

ESIA

BR

AZI

L

PA

KIS

TAN

NIG

ERIA

EAST

AFR

ICA

EAST AFRICAB

AN

GLA

DES

H

RU

SIA

MEX

ICO

JAP

AN

PH

ILIP

PIN

ES

TANZ

ANIA

KENY

A

UGAN

DA

SOUT

H SU

DAN

RWAN

DA

BURU

NDI

0

200

0

POPU

LATI

ON

(MIL

LLIO

N)











SINGLE DATA MARKET









O

F PO

PULA

TIO

N B

ASED

ON

SU

BSC

RIPT

ION

S

0

5

10

15

20

25

30

35

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA52 67

13

20219

262 262

92

27

337

34

02 03 0311

82

0 0

Internetusage rates

Mobilebroadband

Fixedbroadband














C

OVE

R BY

PO

PULA

TIO

N

0

20

40

60

80

100

120

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

2G 3G 4G



































0

10

20

30

40

50

60

70

80

90

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Operator 1

Operator 5

Operator 2

Operator 6

Operator 3

Operator 7

Operator 4

Operator 8



0

10

20

30

40

50

SOUT

H SU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F G

NI P

C

223

44 44 44

392

141

261

354

10

175

279

500MB 1G










Source ITU 2017























Paris Convention

Berne Convention


Madrid Protocol




Rwanda 1984 1984 2011 2013 ucirc





177 176 152 101 96



















42 See wwwawherecom

























0

20

40

60

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

Total Population


O

F PE

OPL

E TH

AT A

RE U

NRE

GIS

TERE

D

53

44

27

49

24

47

19 20 18

40

30











Source Findex 2017






0

20

40

60

80

SOU

THSU

DAN

BURU

NDI

TAN

ZAN

IA

UG

ANDA

KEN

YA

RWAN

DA



O

F AD

ULT

S O

VER

THE

AGE

OF

15

7

56

73

37

31

9

21

3933

51

1




Africell Money uuml


BK mVISA uuml

Dau-Pesa uuml

EcoKash uuml

EzeeMoney uuml

ezyPesa uuml

Leo Manoti uuml

M-PESA uuml

mHose uuml

M-Sente uuml

mCash uuml

Micropay uuml



Orange Money uuml

PAYG Platform uuml

Tangaza Pesa uuml

Tigo Cash uuml

Tigo Pesa uuml

Vodacom M-pesa uuml































0102030405060708090

100

UG

ANDA

BURU

NDI

TAN

ZAN

IA

KEN

YA

RWAN

DA2016

90 85 93 91 91

6979

6982

972014

0

02

04

06

08

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

AFRI

CA

EURO

PE

KENY

A

RWAN

DA

023018

034 034029

072

036042







76 Ibid




















0

50

100

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

O

F PO

PULA

TIO

N76 89

328

56

294 267

























Rwanda 56 41 2

Kenya 92 80 3

Uganda 115 122 12

Tanzania 132 137 15

Burundi 157 164 34














102 See httpsklabrw



















0

20

10

30

40

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

1

16

6

30

16

1

4 7

2018

2016
















0

20

100

40

60

80

SOUT

HSU

DAN

BURU

NDI

TANZ

ANIA

UGAN

DA

KENY

A

RWAN

DA

85

78 74

32

71

80



























Annex A










Mediumndashlong 1st











Regional


Short 2nd


Regional Long 2nd





Regional


Medium 2nd





Regional Medium 1st


Regional


Medium 2nd



Regional


Short 1st







Regional


Short 1st


SINGLE DATA MARKET





Short 1st


Regional


Medium 2nd



Regional


Medium 1st





Regional Medium 2nd




Mediumndashlong 1st


Regional


MediumndashLong 2nd









Regional






Medium 2nd




Short 2nd




Medium 1st



Regional Short 2nd


National Medium 1st



Regional


long (traditional)

2nd


Regional Long 2nd











Mediumndashlong 1st









Medium 2nd







Medium 1st





Regional


Long 2nd








Regional Short 2nd






Medium 2nd





National






Regional


Mediumndashlong 2nd




Short 1st






45

Annex B

SDM Scorecard











Framework Category



Tanzania Uganda



172

(148)

138

(291

153

(218)nd

165

(181)

152

(219) (7)



(GSMA 2017)127

(ITU 2016)

nd

53

95

94

100

100

nd

30

89

95

nd

9197



(ITU 2017)128

(GSMA 2017)129

40

40

78

80

921

939

20

20

85

305

64

4590



(GSMA 2017)130 107 25 643 nd 277 45 75



Connectivity


48 831 699 ~215 744 551 90



(World Bank 2016)1311 11 6 0 2 2 15






~61 690 75 04 ~17 55 90



(ITU 2017)


~52

9

~26

90

~20

37

~67

nd

~13

40

~219

4970




83 262 27 11 92 337 55




223

279

44

44

44

392

141

261

3

54

10

175

1

15





130 Ibid


47

Framework Category



Tanzania Uganda

Connectivity


0 03 02 0 34 03

Connectivity


09 21 02 0 19 19 30



Data


Data



26 37 53 nd 32 33 55

Data


Online services


Online services




1 73 31 nd 39 51 90



4 79 39 7 43 55 85




133 Ibid





138 Ibid



Framework Category



Tanzania Uganda






173

(023)

119

(042)

138

(034)

183

(018)

130

(035)

128

(036) 065



15 days


days2

days







37 43 44 nd 28 31 50



3 5 43 nd 4 43 55





26 29 34 nd 24 24 4

140 Ibid





145 Ibid

146 Ibid

147 Ibid


49

Framework Category



Tanzania Uganda







Above 100



122

(2131)

80

(3095

99

(2736)nd

96

(2797)

102

(2697)



24 43 47 nd 36 33 5





24 41 44 nd 31 34 55




150 Ibid

151 Ibid

















(ITU 2017)158 (GSMA 2017)159

40

40

78

80

921

939

20

20

85

305

64

45







nd 293 0 1355 442 344 No more than 50














51




(UNCTAD 2018)161






In progress

Yes YesIn





No

Travel only (not

digital ID)

Travel


No No

Travel

only

(not digital ID)










(UNCTAD 2018)162








161 Ibid

162 Ibid

163 Ibid












37 34 35 nd 28 9 None



4702 6763 7244 2619 2021 628 80








165 Ibid - page 13


53

Annex C









































55



















































Single data market

bull Cybersecurity


bull E-services









57




































Annex D


59







Source ITU














MICUS (2008)


Koutroumpis (2009)


Czernich (2011)




ITU (2012)





BROADBANDDEPLOYMENT

DIRECTBENEFITS



TOTALFACTOR

PRODUCTIVITY

HOUSEHOLDINCOME

CONSUMERSURPLUS

CONTRIBUTIONTO GDP

GROWTH


DEPLOYMENT




















61










Methodology









Part Objective




Part D





63


Variable Definition

Dependent variable
















Variable

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


5171 2913 2384 2384 854 1095


246 230 254 584 931 483


860570 12190 19024 49984 352 3510


82 85 92 45 20 40






Results187



015


220





65








Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


2 2 2 2 2 2


881835 36570 57072 149952 1056 10530


100 100 100 56 25 50

High Scenario

Keny

a

Tanz

ania

Rwan

da

Uga

nda

Sout

h Su

dan

Buru

ndi


15 15 15 15 15 15


903100 60950 95120 249920 1760 17550


100 100 100 90 68 69





Burundi


52 29 24 24 9 11


58 37 30 34 12 15



Burundi


52 29 24 24 9 11


67 55 52 46 22 19





67






Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 313021 576894 102773 519604 52852 60783

Change 2 2 2 3 1 1


Burundi

Jobs 2016 17143199 23341941 5869284 17921987 4547584 4776926


Change 762848 1801406 500713 1180145 178511 120245

Change 4 8 9 7 4 3190





191 Ibid






69



Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


343 312 39 197 27 10

Change 049 066 047 077 031 034


Burundi

GDP 2016 (US$ millions) 70529 47431 8376 25527 9015 3007


836 976 190 448 93 20

Change 119 206 227 176 104 067











ob

ile b

road

ban

d p

rice

Q

PDemand curve (D)


Mob

ile b

road

band

pric

e

Q

P


Prsquo

Prsquorsquo

71







060 24 27 64 103 53




124 79 87 202 323 167

















-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

73







-

2000

4000

6000

8000

-

10000

20000

30000

40000

2010 2011 2012 2013 2014 2015 2016

Fixed broadband


GDP per capita


-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile broadband

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access









-

20

40

60

80

100

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016

Dial up

Fixed broadband

Mobile access

0 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

75







ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

5

10

15

20

25

30

35

2010 2011 2012 2013 2014 2015 20160 25 50 75 100

2010

2011

2012

2013

2014

2015

2016


ltR$51-60 ltR$61-70 ltR$71-80

ltR$81-90

Donrsquot know

ltR$91-100 ltR$100

-

20

10

40

30

60

50

80

70

100

90

2010 2011 2012 2013 2014 2015 2016













77


















Estimation approach








79









OR








Results










REGRESSION 1


REGRESSION 2



REGRESSION 3









81




















83


Regression 323 324 363 364 303 304 57 58 59 60 313 314 393 394








Significance NS NS





Growth iGDP 0031 0035 0013 0041 0031

Significance NS

Growth years educ








Significance


Significance


0016 0008

Significance NS


0067 0027

Significance NS NS


0043 0032

Significance NS NS


minus0129 0075

Significance NS


1537 0178

Significance NS NS


minus2249 minus0264

Significance NS NS

r2 004 003 009 010 007 005 016 013 002 003 006 005 007 004

f-value 1112 453 1544 1016 1895 718 3131 1297 123 116 1817 737 656 278

years 11 11 11 11 12 12 12 12 10 10 11 11 11 11

observations 770 770 770 770 792 792 516 516 230 230 902 902 770 770


85

New 2018 Results















Regression 10 11 300 301 304 305 400 401 414 415 300L 301L 304L 305L 310 311 314 315

Data 2017 2018 2017




MBB (predicted)




Observations 242 242 242 242 330 330 237 237 314 314 242 242 330 330 550 550 450 450


87



Overall

regression

500 501 502 504 506

1st stage

regression

123 124 123 123 123

Data 2017

Group LM-70

Countries Global


Observations 770

Regression 450 451 452 453 450 454 455 321 300 301 300 300 300

Data 2018 2017




MBB

(predicted)



Macro

Controls


Predicted

MBB gt 10

Dummy

mdash-0026

Significance NS

Inter (MBB

gt 10 MBB

rate)

mdash0026

Significance NS

SSA Country

Dummy

-0022

Significance

Inter (SSA

MBB rate)

0039

Significance

Observations 144 130 108 108 144 232 202 770 242 242 154 154 132 132 110











89





Data 2017

Group LM-70 LMB-73

Countries Global



Regression 320 322 324 330 332 334 320 322 324 300

Data 2017






Macro controls No


Significance








Regression 804 805 802 803 800 801

Data 2017


Countries Global


Mobile BB

(predicted)0126 0119 0110 0114 0001 minus0008

Significance NS NS

Macro controls No

SSA country


Significance NS

Inter (SSA MBB

rate)0034 minus0003 0068




91



Regression 900a 901 902 903 904a 905 910a 911 912 913 914a 915

Data 2017

Group LMB-73 MB-39

Countries Global




Macro Controls No


Significance NS NS


Significance NS


Significance NS NS


Significance NS




mdash0033 0024 0031 0100



Significance NS


Significance NS


ignificance NS


Significance NS

Count 368 277 139 19 158






93







SDM EAST AFRICA

Copyright Statement







Foreword

Executive Summary













34Key enablers


Annex A

Annex B

Annex C

Annex D














